DATE: 2019.05
- Engineering design
- CAD by creating complex geometry via SolidWorks
- Arduino programming
- Control systems design
- Physics simulation in robotics by using Webots
- Prototyping by using a 3d printer
- Test setups
- Selection of mechatronics components
- Building an effective robot algorithms by using C++
- Literature survey for recent academic studies
PROJECT:
Goal: In order to have a more natural flocking behavior implementation, the data acquisition of each individual robot has to be kept as low as possible. On the other hand, in order to achieve a successful flocking behavior and to solve a more complex task, the number of individuals within a swarm robots must be increased. In other words, flocking size should be as much as possible. Consequently, there is need to develop a new swarm of robot platform that can demonstrate the solution of complex problems with large amounts of limited information. In order to achieve this goal, each individual robot should be designed in a minimalistic way and produced as cheaply as possible.
The document discusses swarm robotics, providing an introduction and outline describing biological inspiration, characteristics, algorithms like ant colony optimization and flocking, communication methods, and applications for swarm robotics such as search and rescue missions. Key concepts covered include the decentralized control of large groups of robots based on principles of collective behavior observed in swarms, flocks, and colonies in nature.
The document describes a project that aims to develop an automated system for coordinating robots working in a swarm environment. The system uses image processing to identify nearby robots by scanning QR codes on each robot and adjusting schedules to avoid collisions without human intervention. It discusses modeling swarm robotics and compares it to other multi-agent systems. The goals are to enhance efficiency of swarms through automatic coordination and reduce human errors. Applications include defence operations, sensitive tasks requiring coordination, and medical fields.
A NOVEL PROTOTYPE MODEL FOR SWARM MOBILE ROBOT NAVIGATION BASED FUZZY LOGIC C...ijcsit
Autonomous mobile robots have been used to carry out different tasks without continuous human guidance.
To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them.
Navigation means that the robot can move through the environment to reach a destination. Obstacles
avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient
technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured
environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles
position around them and store their positions in shared memory. By accessing the shared memory, the
other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea,
the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
This document discusses communication techniques in swarm robotics. It summarizes three studies on this topic. The first two studies found that using chemical pheromones for communication between swarm robots resulted in better performance than not using pheromones. The third study used a heterogeneous swarm of footbots and eyebots that communicated via telecommunication; this approach worked well due to synchronization between the bot types. In conclusion, chemical communication works best due to the self-learning nature of the robots, but telecommunication can also be effective if different robot types are synchronized properly. Future work could explore new types of chemical trails that are not limited to land or use combinations of communication approaches.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots In this paper, we will discuss this emerging filed, Swarm Robots. This filed has many applications. We will also discuss these applications in detail.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Design & Development of Vision Controlled Snake Robotvivatechijri
A snake is the only reptile which has the ability to conquer harsh terrains like rock and sand with apparent ease. This project highlights the design, development and testing of a snake-like robot prototype. The snake robot offers high stability than any other wheeled devices. It simulates the serpentine motion of a snake and is controlled by the keyfob transmitter and receiver. The motion commands to snake robot are delivered by four button remote control. The brain of the snake robot is an Arduino microcontroller board which has a wireless camera connected with the board which is placed in the front head portion of the snake and therefore it possesses the ability to map and navigate in its surroundings and also to find the possibility of human life. The applications of these kinds of robots are mainly in space exploration, disaster management, surveillance, etc.
The document discusses swarm robotics, providing an introduction and outline describing biological inspiration, characteristics, algorithms like ant colony optimization and flocking, communication methods, and applications for swarm robotics such as search and rescue missions. Key concepts covered include the decentralized control of large groups of robots based on principles of collective behavior observed in swarms, flocks, and colonies in nature.
The document describes a project that aims to develop an automated system for coordinating robots working in a swarm environment. The system uses image processing to identify nearby robots by scanning QR codes on each robot and adjusting schedules to avoid collisions without human intervention. It discusses modeling swarm robotics and compares it to other multi-agent systems. The goals are to enhance efficiency of swarms through automatic coordination and reduce human errors. Applications include defence operations, sensitive tasks requiring coordination, and medical fields.
A NOVEL PROTOTYPE MODEL FOR SWARM MOBILE ROBOT NAVIGATION BASED FUZZY LOGIC C...ijcsit
Autonomous mobile robots have been used to carry out different tasks without continuous human guidance.
To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them.
Navigation means that the robot can move through the environment to reach a destination. Obstacles
avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient
technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured
environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles
position around them and store their positions in shared memory. By accessing the shared memory, the
other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea,
the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
This document discusses communication techniques in swarm robotics. It summarizes three studies on this topic. The first two studies found that using chemical pheromones for communication between swarm robots resulted in better performance than not using pheromones. The third study used a heterogeneous swarm of footbots and eyebots that communicated via telecommunication; this approach worked well due to synchronization between the bot types. In conclusion, chemical communication works best due to the self-learning nature of the robots, but telecommunication can also be effective if different robot types are synchronized properly. Future work could explore new types of chemical trails that are not limited to land or use combinations of communication approaches.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots In this paper, we will discuss this emerging filed, Swarm Robots. This filed has many applications. We will also discuss these applications in detail.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Design & Development of Vision Controlled Snake Robotvivatechijri
A snake is the only reptile which has the ability to conquer harsh terrains like rock and sand with apparent ease. This project highlights the design, development and testing of a snake-like robot prototype. The snake robot offers high stability than any other wheeled devices. It simulates the serpentine motion of a snake and is controlled by the keyfob transmitter and receiver. The motion commands to snake robot are delivered by four button remote control. The brain of the snake robot is an Arduino microcontroller board which has a wireless camera connected with the board which is placed in the front head portion of the snake and therefore it possesses the ability to map and navigate in its surroundings and also to find the possibility of human life. The applications of these kinds of robots are mainly in space exploration, disaster management, surveillance, etc.
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Swarm robotics is an approach to coordinating multi-robot systems consisting of large numbers of simple physical robots. It is based on swarm intelligence, which models the collective behavior of decentralized, self-organized systems found in nature. Key aspects of swarm robotics include agents that interact with each other and their environment based on simple rules, exhibiting emergent intelligent group behavior. Common swarm intelligence algorithms like ant colony optimization and particle swarm optimization have been applied to optimization problems.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm robotics is an approach to controlling large groups of robots inspired by social insects like ants and bees. It emphasizes emergent behaviors from local interactions between relatively simple robots with only local sensing and communication. This allows for properties like robustness, flexibility, and scalability needed for deploying many robots. Swarm robotics systems consist of many homogeneous robots that are not very capable individually but can collectively perform tasks through self-organization.
A swarm is a collective group of self-propelled entities that move together. Swarm robotics uses large numbers of simple robots that coordinate together without a centralized control through local interactions. Swarm intelligence is an artificial intelligence technique inspired by swarms in nature, using algorithms like ant colony optimization and flocking to achieve collective behaviors from decentralized and self-organized systems. These algorithms were developed to help solve optimization problems. While swarms exhibit benefits like adaptability and novelty, they also have disadvantages like being non-optimal, non-controllable and non-predictable. Swarm robotics has applications in industries, medicine, military and space research.
This document discusses swarm intelligence and how it can be used to design algorithms. It provides examples of how ants exhibit swarm intelligence through their collective foraging behaviors without centralized control. Specifically, it mentions how ant colony algorithms have been designed and applied to solve optimization problems like the traveling salesman problem by simulating the indirect communication of ants through pheromone trails. The document also notes some potential applications of swarm intelligence in robotics and communication networks.
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Swarm intelligence is the collective behavior of decentralized, self-organized systems, whether natural or artificial. It is used in artificial intelligence research. A swarm of robots could work similarly to an ant colony, with each following simple rules leading to self-organization and task completion without direct communication. Researchers have used swarms of simple robots to spell words and play piano through position-based algorithms. Swarm intelligence is also applied to fields like robotics, staff scheduling, and entertainment.
A Central Pattern Generator based Nonlinear Controller to Simulate Biped Loco...Waqas Tariq
This paper mainly deals with designing a biological controller for biped robot to generate biped locomotion inspired from human gait oscillation. The Nonlinear Dynamics of the biological controller is being modeled by designing a Central Pattern Generator (CPG) which is built with the coupling of the Relaxation Oscillators. In this work the CPG consists of four Two-Way coupled Rayleigh Oscillators. The four major leg joints (e.g. two knee joints and two hip joints) are being considered for this modeling. The CPG based parameters are optimized using Genetic Algorithm (GA) to match an actual human locomotion captured by the Intelligent Gait Oscillation Detector (IGOD) biometric device. The Limit Cycle behavior and the dynamic analysis on the biped robot have been successfully simulated on to Spring Flamingo robot in YOBOTICS environment.
The document summarizes the artificial fish swarm algorithm (AFSA), which is a population-based metaheuristic optimization algorithm inspired by fish schooling behavior. It describes how AFSA simulates behaviors like swarming, chasing, and random movement to explore the search space and exploit promising solutions. The algorithm represents potential solutions as individual fish and moves them through the search space based on their visual scope and interactions with neighboring fish. While AFSA has advantages like global search ability and parameter tolerance, it also has drawbacks such as higher time complexity and lack of balance between exploration and exploitation.
This document provides an overview of recent developments in robotics technologies presented by a group of students. It discusses swarm robotics using the Kilobot and Swarmanoid projects as examples. It also summarizes research on shape-shifting robots using origami techniques, mind-controlled robotics using BrainGate, and cloud robotics platforms like RAPP. The document concludes that while robots can perform tasks more accurately than humans, increased reliance on robots may reduce human skills and values if not developed responsibly.
The document outlines the design of a snake robot with the following key points:
1. It proposes designing a snake robot without wheels that uses 8 servo motors and an Arduino microcontroller for locomotion on rough terrain.
2. The robot will be 3D modeled and simulated in MATLAB and Solidworks before hardware implementation.
3. The aims are to study robot kinematics and mechanics, implement hardware and software, and understand locomotion to move like a snake.
4. Expected outcomes include uses for the robot in industries like inspection, rescue missions where it can access hard to reach places, and for military surveillance.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
Overview of different techniques utilized in designing of a legged robotNikhil Koli
This document discusses techniques for designing legged robots. It begins with an overview of legged robots and their advantages over wheeled robots for traversing uneven terrain. Various techniques are then discussed for optimizing linkages, reducing jerk, and enabling navigation. Methods for estimating key parameters like foot profile, linkage dimensions, and gaits are also presented. These include using gear systems, iterative trials, kinematics equations, geometric ratios, time chains, and complex algorithms. The document concludes with a discussion of analysis techniques like force analysis and identifying instant centers of rotation to aid in dimensional analysis.
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...IRJET Journal
1. The document discusses using model predictive control and reinforcement learning to teach a robotic leg to jump a certain height. Model predictive control is used to regulate the leg's dynamics like torque and angles, while reinforcement learning helps the leg adapt through trial and error.
2. Reinforcement learning algorithms like PPO and A2C are applied to give feedback based on successful or unsuccessful jumps. This helps the robotic leg learn over time to precisely jump the target height.
3. Legged robots have an advantage over wheeled robots in navigating uneven terrain. Quadruped robots like Boston University's Mini Cheetah can move quickly over varied surfaces using model predictive control of its leg actuators and sensors.
A robot swarm is essentially a decentralized multi robotics system that can collectively accomplish missions that a single robot could not achieve by itself. It has some unique characteristics that differentiate it from centralized multi robot systems. Swarm robotics is inspired by the swarming nature of insects and birds. It employs a large number of simple robots which can perform complex tasks in a more efficient way than a single robot. It consists of multi robotics in which large numbers of robots are coordinated in a distributed and decentralized way. The goal is to control a large number of simple robots to solve complex tasks. This paper presents an overview of swarm robotics and its applications, benefits, and challenges. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Swarm Robotics: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50035.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/50035/swarm-robotics-an-overview/matthew-n-o-sadiku
Swarm robotics : Design and implementationIJECEIAES
This document summarizes the design and implementation of a swarm robotics system. The goal was to demonstrate swarming and herding behavior using simple robots. Cheap 8-bit microcontrollers were used as the brain for each autonomous robot in the swarm. The robots used infrared sensors to sense each other and limit switches to sense obstacles without direct communication. The design included the hardware components, circuit layout, and software algorithm to enable the robots to aggregate at a location from random starting points and move together as a group. Testing showed the robots could achieve semi-consistent swarming behavior through decentralized control guided by a simple artificial intelligence algorithm.
This document discusses swarm robotics, which is inspired by natural swarm behaviors like insect colonies and schools of fish. Some key points:
- Swarm robotics involves multiple simple robots working together to complete complex tasks through local sensing and decentralized control, similar to natural swarms.
- The robots in a swarm are able to transport objects collectively by pushing them and communicating to evenly distribute the load.
- A swarm of robots is proposed to push a 2D object across a surface by applying force from all sides. Non-holonomic mobile plate robots are used which require careful pushing strategies to avoid collisions.
- The Klann mechanism is discussed as an alternative to wheels to allow robots to traverse uneven terrain
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Swarm robotics is an approach to coordinating multi-robot systems consisting of large numbers of simple physical robots. It is based on swarm intelligence, which models the collective behavior of decentralized, self-organized systems found in nature. Key aspects of swarm robotics include agents that interact with each other and their environment based on simple rules, exhibiting emergent intelligent group behavior. Common swarm intelligence algorithms like ant colony optimization and particle swarm optimization have been applied to optimization problems.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm robotics is an approach to controlling large groups of robots inspired by social insects like ants and bees. It emphasizes emergent behaviors from local interactions between relatively simple robots with only local sensing and communication. This allows for properties like robustness, flexibility, and scalability needed for deploying many robots. Swarm robotics systems consist of many homogeneous robots that are not very capable individually but can collectively perform tasks through self-organization.
A swarm is a collective group of self-propelled entities that move together. Swarm robotics uses large numbers of simple robots that coordinate together without a centralized control through local interactions. Swarm intelligence is an artificial intelligence technique inspired by swarms in nature, using algorithms like ant colony optimization and flocking to achieve collective behaviors from decentralized and self-organized systems. These algorithms were developed to help solve optimization problems. While swarms exhibit benefits like adaptability and novelty, they also have disadvantages like being non-optimal, non-controllable and non-predictable. Swarm robotics has applications in industries, medicine, military and space research.
This document discusses swarm intelligence and how it can be used to design algorithms. It provides examples of how ants exhibit swarm intelligence through their collective foraging behaviors without centralized control. Specifically, it mentions how ant colony algorithms have been designed and applied to solve optimization problems like the traveling salesman problem by simulating the indirect communication of ants through pheromone trails. The document also notes some potential applications of swarm intelligence in robotics and communication networks.
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Swarm intelligence is the collective behavior of decentralized, self-organized systems, whether natural or artificial. It is used in artificial intelligence research. A swarm of robots could work similarly to an ant colony, with each following simple rules leading to self-organization and task completion without direct communication. Researchers have used swarms of simple robots to spell words and play piano through position-based algorithms. Swarm intelligence is also applied to fields like robotics, staff scheduling, and entertainment.
A Central Pattern Generator based Nonlinear Controller to Simulate Biped Loco...Waqas Tariq
This paper mainly deals with designing a biological controller for biped robot to generate biped locomotion inspired from human gait oscillation. The Nonlinear Dynamics of the biological controller is being modeled by designing a Central Pattern Generator (CPG) which is built with the coupling of the Relaxation Oscillators. In this work the CPG consists of four Two-Way coupled Rayleigh Oscillators. The four major leg joints (e.g. two knee joints and two hip joints) are being considered for this modeling. The CPG based parameters are optimized using Genetic Algorithm (GA) to match an actual human locomotion captured by the Intelligent Gait Oscillation Detector (IGOD) biometric device. The Limit Cycle behavior and the dynamic analysis on the biped robot have been successfully simulated on to Spring Flamingo robot in YOBOTICS environment.
The document summarizes the artificial fish swarm algorithm (AFSA), which is a population-based metaheuristic optimization algorithm inspired by fish schooling behavior. It describes how AFSA simulates behaviors like swarming, chasing, and random movement to explore the search space and exploit promising solutions. The algorithm represents potential solutions as individual fish and moves them through the search space based on their visual scope and interactions with neighboring fish. While AFSA has advantages like global search ability and parameter tolerance, it also has drawbacks such as higher time complexity and lack of balance between exploration and exploitation.
This document provides an overview of recent developments in robotics technologies presented by a group of students. It discusses swarm robotics using the Kilobot and Swarmanoid projects as examples. It also summarizes research on shape-shifting robots using origami techniques, mind-controlled robotics using BrainGate, and cloud robotics platforms like RAPP. The document concludes that while robots can perform tasks more accurately than humans, increased reliance on robots may reduce human skills and values if not developed responsibly.
The document outlines the design of a snake robot with the following key points:
1. It proposes designing a snake robot without wheels that uses 8 servo motors and an Arduino microcontroller for locomotion on rough terrain.
2. The robot will be 3D modeled and simulated in MATLAB and Solidworks before hardware implementation.
3. The aims are to study robot kinematics and mechanics, implement hardware and software, and understand locomotion to move like a snake.
4. Expected outcomes include uses for the robot in industries like inspection, rescue missions where it can access hard to reach places, and for military surveillance.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal
Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal
This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
Overview of different techniques utilized in designing of a legged robotNikhil Koli
This document discusses techniques for designing legged robots. It begins with an overview of legged robots and their advantages over wheeled robots for traversing uneven terrain. Various techniques are then discussed for optimizing linkages, reducing jerk, and enabling navigation. Methods for estimating key parameters like foot profile, linkage dimensions, and gaits are also presented. These include using gear systems, iterative trials, kinematics equations, geometric ratios, time chains, and complex algorithms. The document concludes with a discussion of analysis techniques like force analysis and identifying instant centers of rotation to aid in dimensional analysis.
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...IRJET Journal
1. The document discusses using model predictive control and reinforcement learning to teach a robotic leg to jump a certain height. Model predictive control is used to regulate the leg's dynamics like torque and angles, while reinforcement learning helps the leg adapt through trial and error.
2. Reinforcement learning algorithms like PPO and A2C are applied to give feedback based on successful or unsuccessful jumps. This helps the robotic leg learn over time to precisely jump the target height.
3. Legged robots have an advantage over wheeled robots in navigating uneven terrain. Quadruped robots like Boston University's Mini Cheetah can move quickly over varied surfaces using model predictive control of its leg actuators and sensors.
A robot swarm is essentially a decentralized multi robotics system that can collectively accomplish missions that a single robot could not achieve by itself. It has some unique characteristics that differentiate it from centralized multi robot systems. Swarm robotics is inspired by the swarming nature of insects and birds. It employs a large number of simple robots which can perform complex tasks in a more efficient way than a single robot. It consists of multi robotics in which large numbers of robots are coordinated in a distributed and decentralized way. The goal is to control a large number of simple robots to solve complex tasks. This paper presents an overview of swarm robotics and its applications, benefits, and challenges. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Swarm Robotics: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50035.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/50035/swarm-robotics-an-overview/matthew-n-o-sadiku
Swarm robotics : Design and implementationIJECEIAES
This document summarizes the design and implementation of a swarm robotics system. The goal was to demonstrate swarming and herding behavior using simple robots. Cheap 8-bit microcontrollers were used as the brain for each autonomous robot in the swarm. The robots used infrared sensors to sense each other and limit switches to sense obstacles without direct communication. The design included the hardware components, circuit layout, and software algorithm to enable the robots to aggregate at a location from random starting points and move together as a group. Testing showed the robots could achieve semi-consistent swarming behavior through decentralized control guided by a simple artificial intelligence algorithm.
This document discusses swarm robotics, which is inspired by natural swarm behaviors like insect colonies and schools of fish. Some key points:
- Swarm robotics involves multiple simple robots working together to complete complex tasks through local sensing and decentralized control, similar to natural swarms.
- The robots in a swarm are able to transport objects collectively by pushing them and communicating to evenly distribute the load.
- A swarm of robots is proposed to push a 2D object across a surface by applying force from all sides. Non-holonomic mobile plate robots are used which require careful pushing strategies to avoid collisions.
- The Klann mechanism is discussed as an alternative to wheels to allow robots to traverse uneven terrain
Impact of initialization of a modified particle swarm optimization on coopera...IJECEIAES
Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...IJEECSIAES
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial...nooriasukmaningtyas
The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
The document discusses the use of swarm robotics for agricultural applications such as sowing seeds, harvesting, and storing grains. It proposes a system using two communicating robots that can coordinate to perform tasks like plowing, seed sowing, and manuring while avoiding obstacles. The robots use sensors and radio frequency modules to communicate and switch between centralized and decentralized control modes depending on the detection of obstacles.
International Journal of Computational Engineering Research (IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document presents a study on rule-based navigation for autonomous mobile robots in uncertain environments. It describes using a rule-based sensor network approach where rules are formulated based on sensor data to guide the robot's movements. Simulation results show the rule-based technique is effective for path planning to reach targets while avoiding obstacles. The approach is also compared to other techniques to prove its ability to safely and efficiently navigate environments.
This document provides an overview of an introduction to robotics lecture. It outlines the course structure, which will include weekly live lectures and practical sessions. The first week will have a two-hour lecture and two-hour introductory practical session. Subsequent weeks will follow a regular schedule of one-hour lectures on Wednesdays and compulsory three-hour practical sessions on Wednesdays and Fridays. The document also provides summaries of key topics that will be covered over the course of the lectures, including robot motion, sensors, probabilistic robotics, localization, and simultaneous localization and mapping.
With the development of robotics and artificial intelligence field unceasingly thorough, path planning for avoid
obstacles as an important field of robot calculation has been widespread concern. This paper analyzes the
current development of robot and path planning algorithm for path planning to avoid obstacles in practice. We
tried to find a good way in mobile robot path planning by using ant colony algorithm, and it also provides some
solving methods.
This document provides a summary of a technical seminar presentation on multi-robot systems for space applications. The presentation covered topics including what space robots are, why they are used, examples of multi-robot systems, how path planning algorithms work, the key technologies used in space robots, a proposed multi-robot system architecture, the importance of space robots, types of space robots, and future space missions that will utilize robotics. The presentation provided information on space robots through diagrams, flow charts, and explanations of concepts like sensing, planning, control and execution in multi-robot systems.
On Fault Detection and Diagnosis in Robotic Systems.pdfhenibelgacem
The document discusses fault detection and diagnosis (FDD) approaches for robotic systems. It describes two perspectives for analyzing FDD for robots: 1) characteristics of different robotic systems and their impact on FDD challenges, and 2) different FDD approaches and how they address constraints of robotic systems. Key robotic system characteristics include dependency on exteroceptive sensors, autonomous control, deliberation capabilities, dynamic operating contexts, and interaction abilities. The document outlines FDD challenges posed by each characteristic and example approaches to address the challenges.
Similar to MHead - Self-Organized Flocking in Mobile Robot Swarms (20)
2019.11.17 - Mobil Oyun Analizi - Twisty RoadSamet Baykul
Örnek:
* Oyun genel olarak Aquapark.io oyununun ilkel atası gibi görünüyor.
* Oyun landscape modunda tasarlanmış. Bir HC oyun için uygun gözükmüyor.
* “Swipe” hareketi sonucu top dönüşleri yumuşatılmamış. Oldukça mekanik bir his veriyor.
* “Skip” hareketinde değişen oyun mekaniğine göre kontroller adapte olmuyor. Optimize edilmiş bir kontrol mekaniği olmaktan uzak.
* “Skip” hareketi sonrası bir yola tutunulduğunda topun yönünü yeni yolun gidiş açısına göre ayarlamak bazen imkansızlaşıyor. Bu durumda top açık bir şekilde tersine istikamette gitmediği sürece yönünü yola göre otomatik olarak ayarlayamıyor.
* Oyunun neredeyse hiç bir bölümünde kontrol destek algoritmaları kullanılmamış. Kontrol mekaniği dışında bir şey vaat etmeyen oyun yetersiz kalıyor.
2019.11.16 - Mobil Oyun Analizi - Tomb of the MaskSamet Baykul
Örnek:
* Karakter hamlelerinde ve duvar çarpışmalarında “old school” ses efektleri canlanıyor. Her seferinde farklı bir ses efekti devreye giriyor. Bu şekilde oyuna dinamizm katılmış. Oyun yapaylıktan uzaklaşıp gerçek bir “old school” deneyimi yaşamanızı sağlıyor.
* Oyun ögeleri çok zengin tutulmuş ve her biri oyun deneyimini oldukça etkiliyor. Bu da oynanabilirlik ömrünü arttırıyor.
* Oyun her açıldığında haftalık 8 dolarlık bir üyelik teklifi sunuluyor.Yalnızca “balina avı” için bütün oyuncular rahatsız ediliyor. Belki bazı oyuncular bu ekranı görür görmez oyunu oynamaktan vazgeçiyor. Başta anlamsız gibi görünse de yapımcılar oyunun belirli bir popülariteye ulaşması sonucu bu yola gitmiş olabilirler. Bu şekilde oyun viral etkide bu yöntemin negatif etkisini baskılıyor gibi görünüyor. Yine de etik olarak tartışılması gereken bir konu.Ayrıca uzun vadede sektöre zarar verebilir.
* Kontrol mekaniğinde, istenmeyen hareketleri engellemek için “swipe” vektör limiti normalden biraz yüksek. tutulmuş. Hamle yapabilmek için daha fazla zahmete girmeniz gerekiyor.
* Temaya sadık kalmak için UI’da 8 bit metinler ve kontrast renkler kullanılmış. Ancak oyunda yer alan fazla sayıda özellik ve 8 bit metinlerin düşük çözünürlüğü bir araya geldiğinde okunabilirlik seviyesi düşüyor.
* Bir mobil oyunda olabilecek neredeyse bütün kazanç, reklam teklifleri vs özellikleri eklenmiş olmasına rağmen günlük ödül özelliği eklenmemiş.
2019.11.16 - Mobil Oyun Analizi - AmazeSamet Baykul
Örnek:
* Her bölüm bir öncekine göre yeterince farklı bir deneyim sunuyor.
* Bölümler kolaydan zora doğru ustaca sıralanmış. Oyun mekaniği bölüm tasarımcılarının bölüm zorluğunu kolay bir şekilde kontrol etmelerini sağlamış gözüküyor.
* Oyunda her ne kadar çözülmesi gereken labirent değişse de görsel açıdan top ve taranan bölgelerin rengi dışında bir değişiklik olmuyor.
* Oyun bölümleri arasında bir tema farklılığı yok. Muhtemelen farklı temalar denenmiş ve kötü market sonuçlarıyla temel soyut temaya geri dönülmüş gibi duruyor.
* Yine de daha uzun bir kullanıcı ömrü sağlaması bakımından minimalist çizgide aynı ferahlıkta farklı deneyimler sunulabilirdi.
2019.11.15 - Mobil Oyun Analizi - Aquapark.ioSamet Baykul
Örnek:
• Sakin bir şekilde su kaydırağından kayarak risk almayarak bölümler kolay bir şekilde geçilebiliyor.
• Ancak kaydıraktan çıkma özgürlüğü ve geride kalma hissi çoğu zaman oyuncuyu kendi isteğiyle “Skip” hareketini yapmaya itiyor.
• Başarılı bir “skip” hareketi oyuncuya hile yapma zevki sunuyor.
• Başarısız bir “skip” hareketi sonunda oyuncu bu başarısızlığın arkasında kendi rızasının olduğunu hatırlıyor ve bu da oyunun zorluğunu sorgulama şansını oyuncunun elinden alıyor.
• Her seferinde kıl payı kaçırılan bir “skip” hareketi, oyuncunun bir dahaki sefere daha dikkatli olacağı konusunda iç telkinde bulunmasını sağlıyor.
• Bu hareket oyuncuyu klasik “power-up” alma ve engellerden kaçma mekaniğinin sıkıcılığından kurtarıyor.
* Bölüm tasarımı, ilerleyen bölümlerde görsellik haricinde ciddi bir oyun deneyim farkı sunamıyor. Bu tekrar oynanabilirliği düşürüyor olabilir.
* Su kaydırağının içerisinden akan suyun grafik kalitesi oldukça düşük ve akışkan hissi vermiyor. Ana tema ile bağlantılı olduğundan bu bir fırsatın göz ardı edilmesi anlamına gelebilir.
* Oyunda yarıştığımız AI yarışmacıların altında yer alan kullanıcı isimleri ve ülke bayrakları bu yarışmacıların aslında gerçek kişiler olduğu vurgusunu taşıyor. Ancak şu hususlar bunun sahte olduğunu gözler önüne seriyor:
1. Ana karakterimizin her yarışta sonuncu başlaması,
2. Ana karakterimizin neredeyse her zaman diğer yarışmacılardan daha hızlı gitmesi,
3. Diğer yarışmacıların “power-up” lardan kaçmaya çalışması,
4. İnternet bağlantısı olmadığında oyunun hala aynı şekilde devam edebilmesi.
Measurement of Geometrical Errors in Manufacturing FlatnessSamet Baykul
DATE: 2018.11
This is an experiment report which is prepared for ME410 class in METU mechanical engineering department.
In this report, we will measure the straightness of line segments at certain intervals and calculate the flatness of a surface through these measurements. We will discuss how this measurement works. We will also discuss the results and possible errors.
DATE: 2018.10
This is the second and last internship for mechanical engineers in R&D. ME400 summer practice course in METU (ODTÜ).
This report includes all my personal observations and actions in Emek R&D as a mechanical engineering student during 20 business days.
- Purchasing meetings for required components
- Selection of electro-mechanical components
- R&D management stages
My personal observations:
1. General R&D Cycle: It covers how Emek Arge handles a research project and intermediate steps.
2. Projects of Emek R&D: Information about past and current projects of the Emek R&D.
My personal actions:
1. Factory Tour: Some images and videos of products and environment of the manufacturer company. All related digital media will be provided on demand.
2. Staubli Meeting: Notes on the related meeting are compiled in this section. All the related information about the meeting was compiled in an extra document. But some important actions were included in this report as well.
Introduction to Magnetic RefrigerationSamet Baykul
DATE: 2019.06
We have given a lecture to the class in the course of "Refrigeration Systems" in ODTÜ.
Refrigeration technology has an important role over various areas such as medicine, food, manufacturing, and it is a very important element for a comfortable life for the society. It directly affects the people’s life by permiting to store the medicines and foods for long times, manufacturing with very high accuracy, air conditioning applications, etc.
Although refrigeration technology have lots of benefits which has been mentioned above, conventional vapor compression/expansion systems have some weaknesses. Refrigerant fluids that are used in the traditional cooling/refrigeration applications have important effects over the global warming and ozone depletion. To be able to overcome these disadvantages of the refrigeration applications, new thecnologies which does not use harmful matirals such as traditional refrigerants are investigated. One of these developing technologies is magnetic refrigeration systems.
Magnetic refrigeration systems are commonly used in the low temperature applications and it also has usage in air conditioning applications, aerospace technologies and telecommunication technologies.
Magnetic refrigeration has lots of advantages such that:
1. It uses very small amount of energy compared to compressor work inlet of a similar size vapor compression/expansion system.
2. It is highly more compact and makes less noise than the traditional systems.
3. It has a lower operating and maintenance cost.
4. It is environment friendly and does not cause the global warming or ozone depletion.
Although the magnetic refrigeration has lots of benefits which have been described above, because of its high initial cost and need of the very rare materials in the system, it is not very common recent days, however, it has a high potential for the future.
Vortex Tube Usage in Cooling and Liquification Process of Excess Gases in Ghe...Samet Baykul
DATE: 2019.05
- Computational analysis of a vortex tube
- Developing boundary conditions for heat transfer analysis
- CAD by creating a suitable model for heat transfer analysis
- CFD analysis by using ANSYS FLUENT
- Literature survey for recent academic studies
ABSTRACT:
Vortex tubes are simple and common devices which separates a high-pressure gas flow as two different lower gas flows. One of the outlets has a higher temperature than the inlet high pressure gas and other outlet has lower. Most common types of the vortex tubes are counter and parallel flow types. In counter flow type vortex tubes the cold and hot outlets are on opposite sides and in parallel flow type both the outlets are on the same side. Since it is a simple, well known, compact, portable, highly reliable and has a few initial costs, it could be desirable for the specific heating or cooling and refrigeration applications.
DATE: 2019.05
- Design of a gearbox as a power transmission system
- Calculation of mechanical design parameters
- Mechanical design process
- Bearing selection from a given catalog
- Using ISO standards for a mechanical design process
In this project, a suitable gearbox is designed, and bearings are selected for the given prime mover in a screw conveyor machine. Screw conveyors are used for granular material transporting applications such as wheat. The granular medium can be transported efficiently to any desired position, ie. horizontal, vertical or sloped position.
Design of a Lift Mechanism for Disabled PeopleSamet Baykul
DATE: 2019.01
In this project, a lift mechanism for especially disabled people has been designed. It is known as home lift, platform lift, vertical lift or through floor lift. These products operate by moving up and down. The lift mechanism consists of powertrain, linkage system, and a raising platform.
- Design of a a shaft, connecting rods, pins and weldings
- Static force analysis
- Building shear and moment diagrams
- Calculation of mechanical design parameters
Creating Comfortable Air Conditions in Mars for a Sample Volume ShelterSamet Baykul
DATE: 2019.01
In this project, it is aimed to create a theoretical climate conditions for a sample volume room - shelter enough for 6 people to live inside. The shelter is designed to be as a cylinder shaped, which has the radius of 8m and 5m of height. The climate conditions inside the shelter is designed to be identical with earth conditions in comfort, which is 22°C and %50 relative humidity. The psychrometric processes and heat transfer phenomena is calculated and necessary operations are shown.
- Calculation of heat loss and humidity
- Literature survey for recent academic studies related topic
Solvent Recovery System - Feasibility ReportSamet Baykul
This document provides an analysis for establishing a solvent recovery plant. It begins with an introduction to solvents and recovery methods. It then surveys key solvent markets and competitors. The proposed production method is an adsorption system using activated carbon due to the azeotropic nature of the solvent mixture. The document considers plant location, technology, costs, implementation plan and provides a market analysis. The goal is to establish a solvent recovery plant to reduce waste and costs while gaining profit from recycled solvent sales.
Note Elevator - Summer Practice ReportSamet Baykul
DATE: 2016.10
This is the first internship for mechanical engineers. ME300 summer practice course in METU (ODTÜ).
This report covers my all personal observations and actions in Note Elevator as a mechanical engineering student during 20 business days.
- CAD by drawing workpieces via SolidWorks
- Fundemental engineering economy
- Classification of components
- Manufacturing processes and machines
- Efficiency of production line
- Efficiency of management
My personal observations:
1. General Project Cycle: It covers how the company starts and finishes a project and intermediate steps.
2. Manufacturing processes: Detailed explanation of manufacturing processes.
3. Machines and Machine Tools: All used machines and tools in manufacturing.
My personal actions:
1. Sample Workpieces: Technical drawings for 5 different products.
2. Cost Analysis: Cost analysis for 2 different products.
3. Production Line: Production lines in manufacturing area which identified
personally.
4. Classification of Elevator Components: Classification of all the products
according to their assembling groups and translation according to international
elevator industry terminology.
5. Customer Offer Program: A new excel software program for project proposal.
(This task has been given by the manager of the company.)
6. Analysis of the Company Performance: This is related to the efficiency of the team as a foreign observer. (This task has been given by the team in a company
meeting.)
Design of a Stabilizing Handle for Parkinson’s Disease Patients - 2Samet Baykul
DATE: 2019.06.13
This is the final presentation of our project. Our goal is to design a new handle for Parkinson's diseases patients with a function of automatic stabilizing. This handle has been designed as a spoon for the prototype. We expect this spoon will make patients life easy while they eating.
Design of a Stabilizing Handle for Parkinson’s Disease Patients - 1Samet Baykul
DATE: 2019.04.15
This is the mock-up presentation of our project. Our goal is to design a new handle for Parkinson's diseases patients with a function of automatic stabilizing. This handle has been designed as a spoon for the prototype. We expect this spoon will make patients life easy while they eating.
TOPICS:
• Problem Definition
• Functional Decomposition
• Morphological Chart
• Concept Evaluation
• Best Concept
• Detailed Geometric Layout
• Proof of Concept
• Experience Gained from Mock-up
Introduction to Magnetic RefrigerationSamet Baykul
DATE: 2019.05.12
We have given a lecture to the class in the course of "Refrigeration Systems" in ODTÜ.
● Introduction
● History and Developments
● Physical Phenomenas
● Thermodynamics of Magnetic Refrigeration
● Magnetocaloric Materials
● Future of Magnetic Refrigeration
● Developments
● Usage Areas
● Conclusion
Refrigerants and Their Environmental ImpactSamet Baykul
DATE: 2019.04.15
This is a review of an article which introduces Refrigerants and their environmental impact. The study suggests a substitution an adequate refrigerant for hydro chlorofluorocarbon (HCFC) and hydro fluorocarbon (HFC).
Convective Heat Transfer Measurements at the Martian SurfaceSamet Baykul
DATE: 2018.11.28
This is a review of an article which introduces a new sensing method to characterize the convective wind activity on the surface of Mars
TOPICS:
• Introduction
• Objective of the Study
• Cited Studies
• Setup
• Mathematical Model
• Experimental Backing
• Results and Discussions
• Conclusions
• Suggestions
The document discusses different levels of automation, from no automation to advanced artificial intelligence. It outlines 6 practical levels currently used in industry from simple manual machines to computer numeric control machines. It also outlines 4 theoretical future levels including limited artificial intelligence, advanced AI equal to humans, and super machines that command others. While artificial intelligence can eliminate human mistakes, it also poses risks if it destroys civilization. The document concludes that AI has both benefits and dangers depending on its development and application.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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and a decreased count of voltage references, thereby simplifying the control
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An improved modulation technique suitable for a three level flying capacitor ...
MHead - Self-Organized Flocking in Mobile Robot Swarms
1. 1
ME 462 Mechatronics Design
Project Report
Self-Organized Flocking in Mobile Robot Swarms
GROUP “2mm”
Samet Baykul
sametbaykul@gmail.com
1. INTRODUCTION
Flocking is the phenomenon in which a large number of individuals, using limited
information, organize into an ordered motion. It is observed very often in the nature and provides
many advantages for individuals within a group. In flocking, total amount of information obtained by
individuals is increased [1].
Craig Reynolds, who was the first to explain the basics of flocking behavior, described this
behavior in three fundamental rules that each individual should have. These are: attraction,
repulsion and alignment [2]. Attraction means each individual to be pulled by its neighbors to keep
the group together. Repulsion means each individual to stay away from its neighbors and obstacles.
Alignment means each individual to match its velocity and heading direction according to its
neighbors.
Nowadays, collective multi-robot studies which are inspired by nature are becoming more
and more attractive. In order to have a more natural flocking behavior implementation, the data
acquisition of each individual robot has to be kept as low as possible. On the other hand, in order
to achieve a successful flocking behavior and to solve a more complex task, the number of
individuals within a swarm robot must be increased. In other words, flocking size should be as much
as possible. Consequently, there is need to develop a new swarm of robot platform that can
demonstrate the solution of complex problems with large amounts of limited information. In order to
achieve this goal, each individual robot should be designed in a minimalistic way and produced as
cheaply as possible.
Property Statement
Data Acquisition of each Individual Robot Should be minimum
Number of Individual within a Group Should be maximum
Table 1. Fundamental Requirements for Swarm Robot Platform
2. LITERATURE SURVEY
2.1. Flocking Behavior Studies in Swarm Robotics
There have been many previous works in which flocking behavior can be fully implemented
with mobile swarm robots. But in most of the studies, the acquisition of individual information is
large, and the group size is generally small.
Mataric [3] proposed a flocking method on behaviors mentioned above. But in this study
predefined collective homing direction was used as an extra information. Kelly [4] proposed a
flocking method based on a leader-following behavior. A RF system was used for dynamically
electing the leader. Electing leader mechanism is also considered as an extra information for
individuals. Hayes [5] used also a similar mechanism. Furthermore, each individual robot is also
2. 2
informed via an external computer. Although these studies showed a successful implementation of
self-organized flocking on physical mobile robots, in the nature, individuals are not able to know
their own heading direction from an external source.
Turgut [6] proposed a method based on using a digital compass, wireless communication
module and a proximal control without using of an external source. This can be said for the first fully
implemented study of flocking behavior. But only with a group of seven robots have been showed
in a physical environment due to the limitations of communication range and environmental noise
for the digital compass. Baldassarre [7] proposed a set of basic interactions for analyzing group
behaviors but only four physical robots could be used to show. Ferrante, Turgut, et al. (2012) [8],
with a more minimalistic approach, showed that flocking behavior is possible in a random direction
without an alignment control. Nevertheless, the flocking swarm size did not exceed eight robots.
Moeslinger [9] proposed that there is a much easier way to create flocking behavior by only
discretizing the robots’ sensor fields into sectors and using different distance thresholds for
attraction and repulsion in these sectors. Flocking algorithm was quite efficient concerning with
aggregation of scattered robots. But this proposal was not verified in a physical environment. Also,
the authors reported that there was a significant decreasing in mobility of the swarm due to the
flock’s size.
Most minimalistic approaches like Turgut, et al. (2012) and Moeslinger’s studies seem to be
most valuable studies for the future of this project.
2.2. Mobile Swarm Robot Platforms
There are many swarm robot platforms used for swarm robotic studies. A comparison table
was given in Appendix 1, which indicates released year, cost, size, microprocessor, locomotion
technique, battery, communication modules, other physical components, simulators and other pros.
and cons. for each robot platform. There are many other products, but similar products are not
included in this table.
Five important features of a swarm robot platform can be listed as like this (Showed in Table
2): Construction cost, size, sensing capabilities, easy of manufacturing and easy of using. Although
these platforms are claimed to be low-cost, many of them still quite expensive. Even using of Colias,
which is the cheapest robot, in a one-thousand-swarm causes a very high cost for the study. Small
sizes are also important for the use of a large number of individual robots in a limited space. It is
seen that the vibration locomotion reduces the size but decreases the speed also. However, there
are some successful examples such as Alice and Colias are also available for the speed-size trade-
off. Sensing capabilities should be kept minimum by the nature of swarm robotics studies. Some
platforms have lots of capabilities such as Kobot, Foot-Bot, R-one and e-Puck but relatively
expensive and bigger. Finally, it is often difficult for researchers to produce and using these
platforms by using procedures in provided open sources. Considering all of these features, most
promising platforms seem to Alice, Colias and Kilobots.
Property Best Platform Record Overall Situation
Construction Cost Colias $41
Costs are high but getting cheaper
Nominal cost is about $100
Size Alice 2.2cm Nominal value is 4 cm
Minimalist Sensing
Capabilities
Kilobot 2 sensors Highly variable
Easy of
Manufacturing
Kilobot
n/a h for
construction
Easy for only experienced researchers
Easy of Using e-Puck, R-one
n/a # of
features
Easy for only experienced researchers
Table 2. Overall Situations for Swarm Robotics Platforms
3. 3
3. PRELIMINARY DESIGN
In swarm robotic studies, it is hard to test an algorithm by using a large number of individual
robots because of the manufacturing cost and complexity. Simulation tools used to solve this
problem provide limited options due to the inaccuracy. Available robot platforms are still expensive
and over-complicated. To address this problem, this project presents low-cost, autonomous, small-
sized robot platform in order to provide testing of collective algorithms on large number of robots
for people interested in swarm robotic studies. In addition, the new robot platform will be used to
testing of a basic flocking behavior.
3.1. Design Requirements
1. Platform should consist of several robots.
2. Robots should be small enough.
3. Robots should be fast enough.
4. Robots should have enough duty time.
5. Robots should not collide with each other and not move too away from each other.
6. Each robot should be fast and accurate in adjusting of its speed and heading direction
according to swarm.
7. All robots should run autonomously except for leading robots. (For the demo day)
8. Leading robots should be able to sense an external actuator. (For the demo day)
9. Robots should be manufactured at low costs.
In addition, for a more innovative and generic swarm robot platform, some other
requirements should also be considered as follows:
1. Platform should support testing of swarm algorithms in large numbers.
2. Robots should be very small.
3. Robots should be very fast.
4. Robots should have a very long duty time.
5. Robots should be user-friendly.
3.2. Design Criteria’s
Considering the design requirements for only flocking behavior, the robots must have the
following specifications:
1. Platform should consist of at least 2 robots.
2. The diameter of a robot should not exceed 20cm.
3. Robots should be faster than 0.1 robot size/s.
4. In a single session, robots should run for at least 10 min.
5. Each robot should keep the distance between its closest neighbor from 1 to 5 robot sizes.
6. About velocity and heading alignment of each robot, alignment time should not exceed 10
seconds and alignment error should not exceed 90 degrees.
7. In all robots except for 1 robot, the number of external control sources must be 0.
8. Leading robot(s) should sense an external actuator up to a distance of 5 robot size.
9. Total cost of the prototype of a robot should not exceed 300₺.
For additional requirements:
1. Platform should consist of at least 1024 robots.
2. The diameter of a robot should not exceed 3cm.
3. Robots should be faster than 1 robot size/s.
4. In a single session, robots should run for at least 10h.
5. All robots should be charged and programmed simultaneously within a 2h.
4. 4
3.3. Conceptual Designs
There are four conceptual designs in order to provide design requirements. For overall view,
some expected features are given in Table 3.
Table 3. Some Expected Features of Conceptual Designs
3.3.1. M-Head (Mushroom Head)
A simple hemisphere shaped design (Figure 1) mobile robot for flocking swarm
implementation. At the bottom side, two micro DC motors are placed in the reversed direction in
order to gain more space. If there is an imbalance in motion due to asymmetry, the number of
wheels can be increased during testing stage. Control board is mounted on the motors. At the top,
there is a battery as an energy source and a swivel cover in order to access the battery easily.
There are IR’s on the front side. An RGB LED is used to communicate with users and it also
indicates the heading direction of the robot.
Figure 1. (Above) M-Head mobile robot. (Below-Left) Locomotion method. (Below-Right) Other basic
components
3.3.2. VS-Cell (Vibrating Solar Cell)
A battery-fee solution with a solar energy cell (Figure 2). Since with two direct contact
vibrating motors and no battery, this is the smallest one among all the concepts. It has also two
IR’s, one LDR and a programmable control board. Control board has also a structural functionality
as a main body element to hold vibrating motors and solar panel. End edge of the control board is
also touching the ground for the static balance.
Figure 2. VS-Cell mobile robot and basic components.
Property VS-Cell V-Cone M-Head
Cost 50₺ 200₺ 300₺
Size 2.0cm 2.5cm 3.0cm
Speed 1cm/s 5cm/s 20cm/s
Duty Time n/a 1-2h 1-2h
5. 5
3.3.3. V-Cone
A cone shaped simple design (Figure 3) mobile robot. It consists of two micro DC motors
which are placed at a certain angle, two IR’s, one LDR, one RGB LED and a programmable control
board inside. Battery is placed at the bottom and accessible easily by user. Since shafts of DC
motors touch the ground directly, which is a unique solution, no wheels are used and by this way
size is minimalized.
Figure 3. (Left) V-Cone mobile robot. (Right) Basic components.
6. 6
4. DETAILED DESIGN
Red Lines: Needs to be checked and updated
4.1. Short Explanation of the Design
With a direct shaft-contact locomotion technique and simple hemisphere-shaped design, M-
Head (Figure 4) is a relatively cheap, small, fast and user-friendly self-autonomous mobile robot
which is aimed to be used for flocking behavior implementation in swarm robotics. It consists of two
micro DC motors which are placed in a special V-shape at a certain angle (α) to decrease the
required space. There is no gear or wheel inside. Motors are controlled by H-bridge drivers. Unlike
many mobile robots, M-Head has only one pair of IR sensors but still able to perceive its around by
using rotational motion with a special algorithm. It has also an LDR brightness sensor and an RGB
LED light. Batteries are placed at the top and accessible easily by user.
4.1.1. 3D CAD Model
SolidWorks was used for design of M-Head. Related unfinished 3D models are showed in
Figure 4. After the selection of components and compression studies, the general characteristics
are determined as follows (Table 4):
Properties Value
do Diameter 60 mm
h Height 32 mm
W Weight 60 g
t Shell Thickness 2 mm
hg Ground Gap 2 mm
s Shaft Distance 42 mm
α Shaft Angle 57.15°
Table 4. General Design Properties of M-Head
Figure 4. 3D CAD Model of M-Head
4.1.2. Webots Simulation
Webots was used to simulate physical behaviors and control algorithms. Webots is an open
source robot simulator that provides a complete development environment to model, program and
simulate robots. 3D simulation model of M-Head is showed in Figure 5.
7. 7
Figure 5. Simplified 3D simulation model of M-Head
4.2. Selection of Components
4.2.1. Selection of Motor
Since there is no need for angular position control on the motor side, stepper and servo
motors are excluded. Among DC motors, brushless motors are not preferred because of their high
costs. As the small size and low cost are high priority for the robot, it is better to use of coreless DC
motors that can be produced in the smallest size in Figure 6.a. But these motors can rotate at very
high speeds with very low power. Considering the required traction and thermal failure limits, regular
micro DC motors (Figure 6.b) is the best option among others. Comparison of technical data for
coreless and regular DC motors are given in the Table 5.
Figure 6. a. Coreless DC motor. b. Regular DC motor
Coreless DC
Motor
1st Micro DC
Motor
2nd Micro DC
Motor
Voltage 6 V 6 V 6-12 V
Current 0.15 mA (?) 35 mA 35 mA
Motor Length 13 mm 22 mm 23 mm
Motor Radius 4 mm 12 mm 12 mm
Shaft Length 3.5 mm 5 mm 5 mm
Shaft Radius 0.7 mm 1 mm 1 mm
Weight 1 g 8 g 10 g
Speed 40,000 rpm (?) 15,000 rpm (?) 15,000 rpm (?)
Max. Temperature (?) 90ºC (?) 90ºC (?)
Torque (?) (?) (?)
Table 5. Technical specifications for selected DC motors
8. 8
4.2.2. Selection of Proximity Sensor
In generally, for mobile robots, IR and ultrasonic sensors (Figure 6.a) are used to determine
the distance with neighboring robots. The distance measurement range of the ultrasonic sensors is
greater than the IR sensors (Figure 6.b). However, IR sensors are preferred because IR sensors
give more reliable results in indoor operations. The other reason of using IR sensors instead of
ultrasonic sensors is that IR sensors provide higher precision in near distances for low-budget
range. Technical data comparison of preferred sensors is given in the Table 6.
Figure 6. a. Ultrasonic transmitter and receiver pair. b. IR transmitter and receiver pair
Ultrasonic Sensor
Radius 16mm
Voltage 5 V
Frequency 40 Hz
Precision 68 dB
Resolution 10 mm
Max. Distance 400 cm
Min. Distance 2 cm
Angle of Vision 15º
Operation Temperature -20ºC ~ +80 ° C
Weight 1 g
IR Sensor
Radius 5mm
Voltage 1.35 V
Wavelength 940 nm
Max. Distance 30 cm
Min. Distance 2 cm
Angle of Vision 34º
Operation Temperature -40ºC ~ +85 ° C
Weight <1 g
Table 6. Technical specifications comparison of the ultrasonic sensor and IR sensor
4.2.3. List of Components
All components which are used in the construction of M-head is given in Appendix 2.
9. 9
4.3. Compactness
This is the installation of components into the smallest possible volume for M-Head. As a
result, the inner diameter of the hemispherical shell and the axial angle of the motor shaft axis to
the horizontal ground were calculated as respectively 56 mm and 57.15°.
4.3.1. Effect of DC Motor Selection
The effect of motor selection on dimensions was investigated. It was attempted to obtain
more space inside the shell by placing the motors in a V-shape. (Figure 7) In here, inner shell
diameter based on the optimum shaft contact angle was calculated for different motors and
scenarios (with or without middle circuit space). Other geometric limitations are as follows:
• Clearance between the bottom edge of the motor and the ground,
• Clearance between two motors,
• Required circuit space between the motors in the lower area.
Coreless DC motors gives best results because of its very small sizes. But for now, micro
DC motors are selected. Among both micro DC motors, 2nd
DC motor is longer but gives better
results because of the longer shaft length. Difference is not very big but when it is combined with
other factors such as higher torque capacity, durability, quality etc. the use of 2nd
DC motor is more
reasonable. Related works are showed in Appendix 3. All the results are given in Table 8.
Figure 7. Installation of DC Motors
Circuit
Space
Motor
Height
Motor
Dia.
Shaft
Height
Shaft
Dia.
Motor
Gap
Ground
Gap
Thickness α R Di Do
Coreless DC - 12,5 6 5,5 0,8 1 >2 1 46,1 14,17 28,34 30,34
Coreless + 12,5 6 5,5 0,8 10,64 >2 1 55,2 17,94 35,88 37,88
1st Micro DC - 22 12 5 1 1 1 2 55,46 26,08 52,16 56,16
1st Micro DC + 22 12 5 1 2,68 1 2 55,46 26,43 52,86 56,86
2nd Micro DC - 23 12 6 1 1 1 2 49,57 26,13 52,26 56,26
2nd Micro DC + 23 12 6 1 1 0,94 2 49,11 26 52 56
2nd Micro DC + 23 12 6 1 1 2 2 57,15 28 56 60
Table 8. Effect of Motors with Different Sizes on Compactness
(α: angle between axial motor shaft and the ground)
10. 10
4.3.2. Installation of Components
SolidWorks is used as a main tool to install all necessary components into the defined
volume which is determined in the section of “Effect of DC Motor Selection” and based on the angle
α (57.15°) and the diameter (56 mm) of the inner robot shell. The results of optimum installations
are given in Appendix 4.
In Appendix 4. Figure 4.1., the DC motors are installed as in the previous. In addition, a
square hole is drilled on the shell for the heat sink. Two wings are added to fix the motor. A bearing
extending from the shell to the shaft will also be attached to hold the motor from the bottom side.
On the upper side an RGB LED is placed between the motors.
In Appendix 4. Figure 4.2., the important point is that particularly the long edges of the
circuits fit into the shell. At the top in the figure (Rear side of the robot), some part of the socket and
the heat sink is opened out of the shell and this is already required. The socket and the Arduino are
inserted end to end. At the bottom in the figure (Front side of the robot), the IR sensor module is
positioned horizontally. In this way, the total length of the socket, Arduino and horizontally
positioned IR module does not exceed the inner diameter of the shell. On the other hand, the motor
driver must not exceed into the motor site (7 mm above and down of the central horizontal axes)
because its thickness (21 mm) is more than the gap between the motors (18 mm). The battery
beds do not pose a problem in this plane view as shown in the next figure. The other important thing
is that the design of the motherboard is determined according to this installation. Optimum design
of the motherboard is given in Appendix 4 Figure 4.2. b.
In Appendix 4. Figure 4.3., the important point is that particularly the thickness of the
components fit into the shell in especially y-axis. Arduino is placed with 2 mm clearance to the
ground. Since the reset button on Arduino is used by the user, it must be placed downwards. The
motherboard is mounted on Arduino. The total thickness of these two circuits should not exceed
6.33 mm, considering the motor installation (Maximum allowable space under the V-shape motor
installation). In the IR module, the potentiometer is facing downwards and again with 2 mm
clearance with respect to the ground. The IR module must not enter the motor site and should be
located below the lower edge of the battery bed. At the right of the figure (Rear side of the robot),
the socket, the motor driver and the heat sink are placed in colinear from the right side. The left
edge of the motor drive must not enter the motor site. The battery beds are located adjacent to the
motor site on both sides. The RGB LED is located at the top. The IR receiver is located over the IR
module. But details about installation of the IR sensors is given in the next section.
4.4. Installation of Sensors
The ability of robots to communicate effectively with each other depends on the effective
use of sensors. Accurate positioning of the sensors in the physical environment is just as important
as their correct usage on the control side. In working principle of IR sensors, the reflection of the
transmitted infrared light from the object is followed by the receiver. Accordingly, the lower the focus
of the reflected infrared light from the center of the receiver and the higher the intensity of the light,
the better the results are obtained. The effects of the sensors on the design can be listed as follows:
• Opening suitable holes on the shell surface according to 5 mm IR sensor pair.
• Choosing smooth white material on the shell surface to provide maximum reflection for
the IR receiver to collect more incensed light.
• Adjustment of position and angles of the IR sensor holes depending on geometry for
required distance range.
The range of the IR sensor module is 2 cm to 30 cm is given by the vendor. However, this
range can be adjusted by the potentiometer which is located below the robot. It should be noted
11. 11
that there is an inverse relationship between distance and sensitivity for IR sensors. In this way,
different sensitivities can be obtained for different distances in different swarm studies. In this
project, the distance was taken from 20 mm to 100 mm. Robot distance is determined as 60
mm at peak.
The roll angle of the IR sensors and the distance from the vertical axis of the robot center
are set according to distance of 60 mm (distance between the two robots) in the XZ plane (top
view), the focus of the reflected light passing through the IR receiver's central collection point. As a
result, the roll angle was determined as 7.05 degrees and the distance from the center was 7.8
mm. Likewise, the pitch angle and height of the IR sensors are set according to distance of 60 mm
(distance between two robots) in the YZ plane (side view), the focus of the reflected light passing
through the receiver's central collection point. As a result, the pitch angle was 8.3 degrees and its
height were determined as 13.3 mm. All relevant design parameters are given in Table 9.
Table 9. Design parameters of Sensors
The following table (Table 10) shows important parameters about the IR sensors according
to robot distances from 20 mm to 100 mm. The relevant study was obtained by using geometric
parameters in SolidWorks. Commute Distance of Light is the minimum path the light from the
transmitter should go until it reaches to the receiver. A direct reduction in light intensity is expected,
as this value increases. Focus Distance of Reflected Light to the Receiver refers to the distance
of the reflected light focus to the receiver's center. The proximity of the light focus to the receiver’s
center affects the reliability of the output signal. Diameter of spreading Light indicates the
diameter of the circle formed on the reflection surface. As this value increases, the reflected light
intensity decreases. The results of the last two parameters are tabulated for both planes, separately.
Finally, Non-reflected Light Ratio shows the ratio of the non-reflected light to the total transmitted
light, which cannot be reflected because it cannot contact a surface of the opposite robot. Obviously,
as this value increases, the intensity of the incoming light to the receiver decreases.
Distance of
Robots [mm]
YZ Plane (Side View) XZ Plane (Top View)
Commute
Distance of
Light [mm]
Focus
Distance of
Reflected
Light to the
Receiver
[mm]
Diameter of
Spreading
Light [mm]
Focus Height
of Reflection
Side from the
Ground [mm]
Focus
Distance of
Reflected
Light to the
Receiver
[mm]
Diameter of
Spreading
Light [mm]
Non-
Reflected
Light Ratio
[%]
201
59,66 12,33 17,09 9,69 19,17 13,1 -
22 63,52 12,32 18,2 9,42 18,82 14,22 -
24 67,38 12,24 19,32 9,14 18,4 15,36 -
26 73,24 12,08 20,45 8,86 17,93 16,5 -
28 75,1 11,86 21,58 8,58 17,39 17,65 -
302
78,98 11,55 22,65 8,3 16,79 18,81 -
1
The minimum distance which can be measured by the sensors. The visuals of the related studies are given in Appendix
5. Figure 5.1.1 (Side view) and Appendix 5. Figure 5.2.1 (Top view).
2
In the side view, the lower boundary of the transmitted light starts to come to the ground instead of the robot surface.
Relevant visual is given in Appendix 5. Figure 5.1.2. In addition, in the top view, at this distance, the focus of the
reflected light passes through the transmitter again. Appendix 5. Figure 5.2.2.
Parameter Design Plane Value
Optimum Robot Distance - 60 mm
Roll Angle
XZ Plane (Top View)
7,05°
Distance from the Center Line 7,8 mm
Pitch Angle
XZ Plane (Top View)
8,3°
Distance from the Bottom Line 13,3 mm
12. 12
32 82,86 11,17 23,74 8,02 16,12 19,98 -
34 86,76 10,73 24,99 7,74 15,39 21,17 -
36 90,64 10,21 26,37 7,46 14,6 22,37 -
38 94,54 9,63 27,87 7,18 13,74 23,58 -
40 98,46 8,98 29,48 6,89 12,82 24,81 -
42 102,36 8,25 31,17 6,61 11,83 26,06 -
44 106,28 7,47 32,93 6,33 10,78 27,33 -
46 110,2 6,61 34,76 6,05 9,67 28,62 -
48 114,12 5,68 36,65 5,76 8,49 29,94 -
50 118,06 4,69 38,58 5,48 7,24 31,3 -
52 122 3,63 40,57 5,19 5,92 32,69 -
54 125,94 2,5 45,59 4,91 4,54 34,13 -
56 129,9 1,31 44,65 4,62 3,09 35,64 -
58 133,84 0,04 46,75 4,34 1,57 37,23 -
603
137,8 1,3 48,88 4,05 0 38,94 -
62 141,75 2,71 51,03 3,77 1,71 40,83 -
64 145,74 4,18 53,22 3,48 3,45 43,1 -
66 149,72 5,73 55,44 3,19 5,26 47,12 -
684
153,7 7,35 57,68 2,91 7,17 - 1,43
70 157,7 9,04 59,95 2,62 9,14 - 2,87
72 161,68 10,83 62,24 2,33 11,21 - 4,23
74 165,68 12,67 64,57 2,04 13,34 - 5,67
765
169,7 14,6(3) 66,93 1,75 15,58 - 6,8
78 173,7 16,62 69,31 1,46 17,9 - 8,03
80 177,72 18,7 71,73 1,17 20,31 - 9,2
82 181,74 20,88 74,19 0,88 22,81 - 10,33
84 185,78 23,14 76,69 0,59 25,41 - 11,43
86 189,8 25,49 79,24 0,3 28,1 - 12,5
88 193,84 27,92 81,85 0,01 30,9 - 13,5
906
197,9 30,46 84,52 -0,28(4) 33,79 - 14,4
92 201,94 - 87,28 -0,58 36,82 - 15,46
94 206 - 90,17 -0,87 39,94 - 16,37
96 210,06 - 93,24 -1,16 42,12 - 17,27
98 214,12 - 96,67 -1,46 44,36 - 18,13
100 218,2 - 101,74 -1,75 46,65 - 19
Table 10. Effect of Motors with Different Sizes on Compactness.
Analysis of sensor behaviors according to distances between two neighboring robots is
given in Appendix 5.
3
This is the optimum distance. The design parameters for the sensor pair are based on this distance. In both planes (Side
and top views) the focus of the reflected light passes through the IR receiver's center. As it moves away from this
distance, the focal point begins to move away from the IR receiver’s center again. The visuals of the related studies are
given in Appendix 5. Figure 5.1.3 (Side view) and Appendix 5. Figure 5.2.3 (Top view).
4
At the top view, a certain portion of the transmitted light starts to miss the surface of the opposite robot. The ratio of
non-reflected light to the total transmitted light is tabulated in the corresponding column (Non-Reflected Light Ratio).
Relevant visual is given in Appendix 5. Figure 5.2.4.
5
At the side view, the focus of the reflected light begins to fall below the shell surface of the light transmitter robot.
Relevant visual is given in Appendix 5. Figure 5.1.4.
6
This is considered as the farthest distance to be studied for this project. The visuals of the related studies are given in
Appendix 5. Figure 5.1.5 (Side view) and Appendix 5. Figure 5.2.5 (Top view).
13. 13
5. MATHEMATICAL MODEL
5.1. Direct Shaft-Contact Mechanism
Since M-Head has no gear and no wheel, it uses directly motor shafts for traction. Each
motor shaft is in contact with the ground at a certain angle (α = 62.17º). This angle is optimized for
the compactness of the robot. The shaft is rotated at a high speed (up to 15000rpm) on the surface
to provide traction due to the friction force. Since the shaft slides over the surface, the linear speed
of the wheel at the contact point will be different from the overall speed of the robot. Nevertheless,
there is still a correlation between overall robot speed, motor rotation speed, reaction force and
friction coefficient. This correlation can be expressed by the following formula:
!" = $(&) ∙ * ∙ + 1
The total robot weight (W) is about 60g. There are 4 contact point between the robot and
the surface. Then reaction force (N) is 140 mN. Friction coefficient (k) was taken as 0.25. In the
case of insufficient traction, the motor speed can be increased with the motor driver. In the
simulation and in the real world, the relationship between motor speed and traction is obtained and
showed in Table 11 and Table 12.
Motor Speed Robot Speed Stability
2500 rpm 8 cm/s YES
5000 rpm 16 cm/s YES
10000 rpm 21 cm/s NO
15000 rpm 32 cm/s NO
Table 11. Motor Speed vs Robot Speed
()
Table 12. (A test will be done after the manufacturing)
5.2. Thermal Considerations
Since gear regulator is not used, there is a linear relationship between motor speed and
torque. In other words, it is not possible to obtain the necessary traction without increasing the
motor speed. So high speed on the contact point is inevitable. On the other hand, the shaft slides
on the surface as the wheel is not used. Due to friction, it is expected to the shaft heat up. In addition,
because the engines are small, the heat dissipation is low as an extra problem. For these reasons,
the temperature must be kept under control so that the motors can operate safely in the expected
time. So, the motor speed and thus the traction force will be lower than the actual limit to prevent
any thermal failure. Depending on the motor speed, the thermal durability comparison of the motors
is showed in Table 13.
In order to ensure that the heat emitted from the motors and the motor driver is dissipated
by free convection, heat sinks have been placed on ventilation channels on the outer shell of the
robot.
According to the results of the experiment, an embedded thermal control system was also
provided to the robots. Details about this system are included in the control design section.
()
Table 13. (A test will be done after the manufacturing)
14. 14
5.3. Motion Mechanism
5.3.1. Translational Motion
When the two motors rotate in the same direction, the translational motion is achieved.
Rotational motor speed vs overall robot speed change is shown in Table 11 and Table 12.
5.3.2. Rotational Motion
When the two motors rotate in opposite directions, the robot rotates around itself. By using
the simulation tool, the obtained graph of the motor speed and depending angular rotation speed
of the robot is shown in the Table 14.
Motor Speed Period
Physical
Stability
Stability
Duration
Accuracy Results
90 rad/sec 3.200 sec YES 20 sec Not Accurate
180 rad/sec 1.888 sec YES 19 sec Not Accurate
270 rad/sec 1.248 sec YES 15 sec Not Accurate
360 rad/sec 0.992 sec YES 11 sec Not Accurate
450 rad/sec 1.888 sec NO n/a Not Stable (WORST)
60 rad/sec 4.768 sec YES 23 sec Not Accurate
120 rad/sec 2.400 sec YES > 3 min +3.1 mm
150 rad/sec 1.920 sec YES 17 sec Not Accurate
210 rad/sec 1.632 sec YES 18 sec Not Accurate
240 rad/sec 1.632 sec YES 17 sec Not Accurate
300 rad/sec 1.248 sec YES 14 sec Not Accurate
80 rad/sec 3.552 sec YES 20 sec Not Accurate
100 rad/sec 2.848 sec YES 20 sec Not Accurate
110 rad/sec 2.624 sec YES 19 sec Not Accurate
130 rad/sec 2.208 sec YES 19 sec Not Accurate
140 rad/sec 2.048 sec YES 19 sec Not Accurate
160 rad/sec 2.080 sec YES 19 sec Not Accurate
94 rad/sec 3.040 sec YES > 3 min -2.7 mm
116 rad/sec 2.464 sec YES > 3 min -2.7 mm
118 rad/sec 2.432 sec YES > 3 min -3.8 mm
122 rad/sec 2.336 sec YES 20 sec Not Accurate
124 rad/sec 2.304 sec YES 19 sec Not Accurate
112 rad/sec 2.560 sec YES > 3 min +1.2 mm (BEST)
114 rad/sec 2.496 sec YES > 3 min -4.5 mm
Table 14. Motor Speed vs Rotational Motion Results
15. 15
5.4. Mechanical Parameters
All the important parameters for optimum traction and overall speed for the robot are given
in the Table 15.
Input/output Parameter Symbols Description Values
Input Values
w Angular velocity of
motor
15000 rpm
(250 rps)
d Diameter of motor
shaft
1 mm
k Friction constant
(Clean-cut steel and
wood)
0.25
W Weight of the robot 60 g
t Operational time 2400 s
Tmax Maximum allowable
temperature
120ºC (?)
Output Values
Vs Max. linear velocity of
the shaft
78.5 cm/s
N Reaction force (for
each shaft)
140 mN
Ff Friction force 35 mN
Ft Traction force (?)
V Max. velocity of robot (?)
Table 15. Mechanical parameters for optimum traction and overall velocity of the M-Head
16. 16
6. CONTROLLER DESIGN
6.1. Low-Level Control Systems
6.1.1. Motor Power Control
When the robot starts first, more power is supplied to the motors. When the robot reaches
the desired speed, the motor power is reduced and stabilized. In this way, both the engine's heating
is delayed, and energy is saved. And this is also required to compensate time loss due to the inertia
effect. Related control diagram has been showed in Figure 8.
Figure 8. Motor Power Control Diagram
17. 17
6.1.2. Initial Calibration Control
In the modeling of most swarm algorithms, it is important to starting of the robots as
simultaneously. However, in the current version of M-head, it is not possible to program the robots
at the same time (They need to be programmed one by one). So, all the robots need to be calibrated
before starting manually.
This could be achieved by using a remote trigger signal which starts the processing at the
same time. As proposed solution in this document, robots use their brightness sensors. The
successive 3 straight-line-light-signal initiates all the robots. The successive 2 straight-line-light-
signal light signals stops the robots. The successive 4 straight-line-light-signal resets the robots.
The corresponding control mechanics were given in Figure 9.
Figure 9. Initial Calibration Control Diagram
6.1.3. Thermal Control
As stated in the Mathematical Model section, the motor shafts slide on the surface. High-
speed rotating shafts lead to increase motor temperature due to friction. Since the heat dissipation
rate of small-sized DC motors is slow, it is critical to control the motor temperatures in order to
prevent any thermal failure. Since the excessive operation time will endanger the motors, thermal
control system turns off the robot motors automatically after 30 (?) minutes from the start. The user
is informed by the RGB LED in this cooling state. As the motors are small, the cooling time does
not last long. After a certain period of time the robot continue to its operation and terminates the
cooling warning state. Related control diagram has been showed in Figure 10.
Figure 10. Thermal Control Diagram
18. 18
6.1.4. Diagnosis Control Mode
This mode is used to verify the operation of basic functions such as motor speed control,
battery status, sensor readings, current heading direction and light sensitivity. Related control
diagram has been showed in Figure 11.
Figure 11. Diagnosis Mode Control Diagram
6.2. High-Level Control Systems
6.2.1. MODE 1: Rotational Period Time Measurement Control
When the robot is started first, it does not have any information about itself and its
surroundings. Firstly, the robot starts to search for an object that it can detect. When it encounters
an object for the first time, it rotates approximately two and a half rounds around itself and learn
one turning period time. Knowing period time provides the necessary heading information
depending on the returning. RPTM is terminated after the period information is learned.
Figure 11. Rotational Period Time Measurement Control Diagram
19. 19
6.2.2. MODE 2: Magnetic Pathfinder Algorithm
Since the robot has a limited perception capability, it must collect information as much as
possible for a proper flocking implementation. Instead of wandering around randomly, the robot
records the places which it travels and the objects it encounters on a map. The contribution of this
algorithm for the flocking implementation can be listed as follows:
• Possibility of encountering increases for robots. This decreases the required time for
aggregation especially for lost robots.
• Robots remember the obstacles they have encountered before and avoid in the next time.
Thus, they do not spend unnecessary time by scanning against the same obstacle. They
gradually begin to give instant responses for the same objects,
• Robots estimate the possible locations of other robots which they encounter. In this way,
swarm cohesion can be increased.
The pathfinder algorithm used in here is specifically developed for this project. There is no
goal for finding the shortest path. It is not designed to find complex paths like labyrinths. It is
designed especially for the paths which have relatively a smaller number of obstacles. Considering
all of these, this algorithm requires less memory and processing power than generic algorithm such
as A * pathfinder algorithm. It is considered as a more optimal option for lower processing power
such as microcontrollers. The differences between the two algorithms are given in Table 16.
Magnetic Pathfinder
(M-Head)
A* Pathfinder
(Generic)
Shortest path results Occasionally Often
Convenience for complex
roads
No Yes
Memory Usage Low High
Processor Usage
Medium (can be
optimized)
High
Usage Area Microcontroller Microprocessor
Table 16. Comparison of Magnetic Pathfinder and A* Pathfinder Algorithms
6.2.3. MODE 3: Path follower Algorithm
It enables the robot to access to a targeted position on the map. It can be used without a
pathfinder algorithm. It works like a joystick in a video game.
6.2.4. MODE 4: Geometry Recognition Algorithm
It can distinguish the objects which are encountered as moving and stationary. The
obstacles can be distinguished as walls, an ordinary obstacle, a robot, a wall corner etc. The related
diagram is given in Figure 12.
21. 21
6.2.5. MODE 5: Locking Algorithm
It learns the relative velocity and position vectors of a neighboring robot. This provides the
robot to connect and move with the robot with a proper alignment.
(Not implemented yet)
6.2.6. Finetuning
Property Name Description
Optimal
Value for
Webots
Optimal
Value for
Real World
UPDATE_DELAY
Defines the expected time delay between two
operations for the microcontroller
32 30
MAP_WIDTH
Defines the cell number of the arm that opens to
the sides of the map. Total number of the cells in
the map, C is as follows
C = (MAP_WIDTH x 2 + 1)2
The map always defines a 2D square area.
3 6
TARGET_WIDTH
Defines the width of the scanned sub-map in
pathfinding mode. Total number of the cells in
the target map, T is as follows
T = (TARGET_WIDTH x 2 + 1)2
The target map always defines a 2D square
area.
2 3
PATH_LONG
Defines the total memory for the pathfinder. It
has a direct impact on the processing power.
25 10?
22. 22
SPEED_MAX
Defines the maximum speed. The robot uses
this speed when it is lost in panic and is sure
where to go.
260 260?
SPEED_NOM
Defines the nominal speed for a calm
wandering.
130 130?
SPEED_MIN
Defines the minimum speed. The robot uses this
speed when it is not sure where it will go.
65 65?
SPEED_ROT
Defines the rotation speed. This speed must be
optimized for smooth scanning operations.
112 112?
SEGMENT
(CELL_WIDTH)
Defines the width of each of the cells on the
map. The map should be optimized with
MAP_WIDTH when describing the real area. For
example: On low-resolution maps, this value
must be increased in order to cover the entire
area.
23 23?
FIRST_APPROACH
Defines the value of the maximum interference
of IR rays when an obstacle is first encountered.
600 600?
DS_TOLERANCE Defines the tolerance range for the IR sensor. 150 150?
DIAGNOSIS Starts the robot with diagnostic services. - -
DIAG_PERIOD
It is used for optimization of the parameter,
SPEED_ROT.
- -
Table 17. Finetuning Parameters
24. 24
Experimental Results (2 pages)
Put tables and figures summarizing the results here. Put relevant graphs. Discuss the results,
specifically focusing on the effect of controller parameters on the performance of your design.
Compare the results of simulation and real robot experiments.
25. 25
Discussion and Conclusion (1 page)
Discuss the results in general. Summarize the procedure you followed. Make general conclusions.
Put future work. Discuss the results in general. Summarize the procedure you followed. Make
general conclusions. Put future work. Discuss the results in general. Summarize the procedure
you followed. Make general conclusions. Put future work.
26. 26
REFERENCES
[1] Clark, C. W., & Mangel, M. (1984). Foraging and flocking strategies: information in an
uncertain environment. The American Naturalist, 123(5), 626-641.
[2] Reynolds, C. W. (1987, August). Flocks, herds and schools: A distributed behavioral model. In
ACM SIGGRAPH computer graphics (Vol. 21, No. 4, pp. 25-34). ACM.
[3] Mataric, M. J. (1993, April). Designing emergent behaviors: From local interactions to collective
intelligence. In Proceedings of the Second International Conference on Simulation of Adaptive
Behavior (pp. 432-441).
[4] Kelly, I. D., & Keating, D. A. (1996). Flocking by the fusion of sonar and active infrared sensors
on physical autonomous mobile robots. In Proceedings of The Third Int. Conf. on Mechatronics
and Machine Vision in Practice (Vol. 1, pp. 1-4).
[5] Hayes, A. T., & Dormiani-Tabatabaei, P. (2002). Self-organized flocking with agent failure: Off-
line optimization and demonstration with real robots. In Proceedings 2002 IEEE International
Conference on Robotics and Automation (Cat. No. 02CH37292) (Vol. 4, pp. 3900-3905). IEEE.
[6] Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking in mobile
robot swarms. Swarm Intelligence, 2(2-4), 97-120.
[7] Baldassarre, G., Nolfi, S., & Parisi, D. (2003). Evolving mobile robots able to display collective
behaviors. Artificial life, 9(3), 255-267.
[8] Ferrante, E., Turgut, A. E., Huepe, C., Stranieri, A., Pinciroli, C., & Dorigo, M. (2012). Self-
organized flocking with a mobile robot swarm: a novel motion control method. Adaptive
Behavior, 20(6), 460-477.
[9] Moeslinger, C., Schmickl, T., & Crailsheim, K. (2009, September). A minimalist flocking
algorithm for swarm robots. In European Conference on Artificial Life (pp. 375-382). Springer,
Berlin, Heidelberg.
[10] Turgut, A. E., Gokce, F., Celikkanat, H., Bayindir, L., & Sahin, E. (2007). Kobot: A mobile
robot designed specifically for swarm robotics research. Middle East Technical University, Ankara,
Turkey, METU-CENG-TR Tech. Rep, 5(2007).
[11] Brutschy, A., Pini, G., & Decugniere, A. (2012). Grippable objects for the foot-bot. Technical
Report TR/IRIDIA/2012-001). IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.
[12] Garnier, S., Tache, F., Combe, M., Grimal, A., & Theraulaz, G. (2007, April). Alice in
pheromone land: An experimental setup for the study of ant-like robots. In 2007 IEEE Swarm
Intelligence Symposium (pp. 37-44). IEEE.
[13] Arvin, F., Murray, J., Zhang, C., & Yue, S. (2014). Colias: An autonomous micro robot for
swarm robotic applications. International Journal of Advanced Robotic Systems, 11(7), 113.
[14] Droplets. (2014, May 03). Retrieved from http://correll.cs.colorado.edu/?page_id=2687
[15] Szymanski, M., Breitling, T., Seyfried, J., & Wörn, H. (2006, September). Distributed shortest-
path finding by a micro-robot swarm. In International Workshop on Ant Colony Optimization and
Swarm Intelligence (pp. 404-411). Springer, Berlin, Heidelberg.
27. 27
[16] Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., & Nagpal, R. (2014). Kilobot: A low cost
robot with scalable operations designed for collective behaviors. Robotics and Autonomous
Systems, 62(7), 966-975.
[17] One. (n.d.). Retrieved from http://mrsl.rice.edu/projects/r-one
[18] Cianci, C. M., Raemy, X., Pugh, J., & Martinoli, A. (2006, September). Communication in a
swarm of miniature robots: The e-puck as an educational tool for swarm robotics. In International
Workshop on Swarm Robotics (pp. 103-115). Springer, Berlin, Heidelberg.
[19] What is an Ultrasonic Sensor? (n.d.). Retrieved from
http://cmra.rec.ri.cmu.edu/content/electronics/boe/ultrasonic_sensor/1.html
28. 28
APPENDIX 1:
Comparison of Robot platforms
Picture Platform Released Cost Size Microprocessor Locomotion Battery Communication
Other
Components
Simulator Comments
Kobot
[10]
2007 $800 12cm
20 MHz 8-bits
14.3KB
PIC18F4620A
2 x DC
Motors with
2 x Wheels
2000 mAh
LiPo
Battery
Operation:
10h
8 x IR Proximity
IEEE
802.15.4/ZigBee
Compliant XBee
Wireless Module
(Range: 20m)
Digital
Compass
Camera
CoSS
+ Group Programming
+ Kin-detection
- Replaceable battery
which is recharged
manually
Foot-Bot
[11]
2012 ? 17cm ? Treels ?
24 x IR
Proximity
12 x RGB LEDs
Turret force
sensor
Camera
Gripper
ARGoS
+ Rotatable turret that
consists of a grippable
ring and a gripper.
Alice
[12]
2007 ? 2.2cm
20 MHz 8-bit
14KB
PIC16LF877
2 x Swatch
Motors
Speed:
4cm/s
LiPo
Battery
Operation:
10h
4 x IR Proximity
RF Modem
ANT Module
(Optional)
Camera
(Optional)
Gripper
(Optional)
Webots
+ A very small
package size
+ Kin-detection
+ Employed
in various swarm
research applications,
such as the
embodiment of
cockroach
aggregation
- The commercialized
Alice was previously
around a few hundred
pounds
29. 29
Colias
[13]
2013 $41 4cm
8 MHz 8-bits
ATMEL AVR 8
2 x Micro
DC Motors
H Bridge DC
Motor Driver
Speed:
35cm/s
600 mAh
LiPo
Battery
Operation:
1-3h
IR Proximity Light ?
+ Colias uses three IR
proximity sensors to
avoid collisions with
obstacles and other
robots within less than
10 mm.
+ Motors are
controlled individually
using a pulse-width
modulation (PWM)
technique
Droplets
[14]
? ? 4.4cm ?
Vibration
Motors
Operation:
24h
? Light ?
+ Large-scale
swarming researches
+ Droplet-to-droplet
reprogramming
Jasmine
[15]
2006 $130 3.5cm
20 MHz 8-bits
32KB
Atmega328
2 x Small
Gear-Head
Motors
2 x Wheels
LiPo
Battery
Operation:
1-2h
6 x IR Proximity
Light
(Optional)
Color
(Optional)
Gripper
Breve
Simulation
Environment
+ Group Charging
+ Aluminum Structure
+ Kin-detection
+ Played the role of a
honeybee
in several aggregation
(BEECLUST)
scenarios
Kilobot
[16]
2013 $120 3.3cm
20 MHz 8-bits
32KB
Atmega328
2 x Sealed
Coin
Shaped
Vibration
Motors
Speed:
1cm/s
Operation:
3-24h
IR Proximity
RGB LED
Light ?
+ Group Charging
+ Group Programming
+ It uses a slip-stick
principle for motion
which reduces its
cost, since the robot
does not use motors
or wheels.
- The motion method
has several
drawbacks, such as
that the achieved
speed is low, which
limits its application in
swarm scenarios.
30. 30
R-one
[17]
2012 $220 10cm
50MHz 32-bits
256KB ARM
Cortex M3
Speed:
25cm/s
2000 mAh
LiPo
Battery
Operation:
4h
8 x IR Proximity
12 x RGB LEDs
RF Modem
Light
4 x Analog
Cds
3D Gyro
3D
Accelerometer
Encoders
3 x User
Mode Buttons
?
+ Research and
teaching purposes. It
was used in several
studies on swarm
robotics.
e-Puck
[18]
? $1300 7.5cm
30 MHz 16-bits
144KB
PIC30F6014A
2 x Stepper
Motors
Speed:
13cm/s
Operation:
1-10h
8 x IR Proximity
Bluetooth
802.15.4 ZigBee
Camera
Speaker
3 x
Microphones
Accelerometer
Webots
+ Mainly designed for
education in the
engineering field
+ Bluetooth
Programming
- $400 is needed to
obtain an additional
range
and bearing module
31. 31
APPENDIX 2:
Component List
Picture Component Dimensions Description
2 x Micro DC Motor
Motor:
D: 12 mm
H: 23 mm
W: 10 g It is used for traction.
Shaft:
D: 1 mm
H: 6 mm
Arduino Pro Mini
328 5V 16MHz
l: 33 mm
w: 18 mm
t: 3 mm
W: 2 g
Arduino Pro Mini is
used as a main
microprocessor.
LM393 IR Sensor
Module
l: 48 mm
w: 15 mm
t: 8 mm
W: 3 g
It is used for obstacle
detection
FT232RL Converter No effect on design
FTDI is used as a
converter from USB
to TTL in order to
program Arduino
from an external
computer
TB6612FNG DC
Motor Driver
l: 21 mm
w: 21 mm
t: 3 mm
W: 2 g
DC Motor Driver is
used to control
speed of DC motors.
2 x CR2032 3V – 210
mAh Coin Battery
D: 20 mm
t: 3.2 mm
W: 3 g
It is used to energy
source for the robot.
6-pin Female
Header Socket
l: 16 mm
w: 8,5 mm
t: 2,6 mm
W: 1 g
This header is used
as a connector from
FTDI to Arduino Pro
Mini.
2 x CR2032 Vertical
Coin Battery Bed
l: 22 mm
w: 23 mm
t: 6.4 mm
W: 2 g
Battery bed is used
to hold battery and
provides energy
transmission from
battery to circuits.
32. 32
3mm Brightness
Sensor
D: 5 mm
t: 2 mm
W: 1 g
A 3mm diameter light
sensor is used to
allow light-sensitive
control programming.
RGB LED
D: 5 mm
h: 8.7 mm
W: 1 g
A 5mm diameter
RGB LED is used to
communicate with
the user
1 x 14x14x6 mm Al
Heat Sink
2 x 8x8x6 mm Al
Heat Sink
-
Smaller heat sinks
are used to increase
heat dissipation rate
of DC motors. Bigger
one is used for the
motor driver.
33. 33
APPENDIX 3:
Effect of DC Motor Selection on the Compactness
Figure 3.1. Coreless DC Motor without Circuit Space (α = 46.1°, Rin = 14.17 mm, Dout = 30.34)
(Best Choice but not selected because of thermal and traction limits)
Figure 3.2. Coreless DC Motor without Circuit Space (α = 55.2°, Rin = 17.94 mm, Dout = 37.88)
34. 34
Figure 3.3. 1st
Micro DC Motor without Circuit Space (α = 55.46°, Rin = 26.08 mm, Dout = 56.16)
Figure 3.4. 1st
Micro DC Motor with Circuit Space (α = 55.46°, Rin = 26.43 mm, Dout = 56.86)
35. 35
Figure 3.5. 2nd
Micro DC Motor without Circuit Space (α = 46.57°, Rin = 26.13 mm, Dout = 56.26)
Figure 3.6. 2nd
Micro DC Motor with Circuit Space (α = 49.11°, Rin = 26 mm, Dout = 56)
(Optimum Choice)
36. 36
APPENDIX 4:
Installation of Components
Figure 4.1. Installation view on xz-plane: RGB LED is at the top, DC Motor is at the side and contacts with inner surface of the shell, aluminum
heat sink is mounted on the motor, required circuit space is at the middle of bottom
37. 37
Figure 4.2. a. Installation view on xz-plane: All end points of all the components are adjusted with respect to the inner surface of the shell.
39. 39
Figure 4.3. Installation view on yz-plane. All components are mounted with respect to others and the limits of inner shell diameter.
40. 40
APPENDIX 5:
5.1. Behaviors of IR Sensors with respect to Different Distances on Side View (YZ Plane)
Figure 5.1.1. Side view of two robots (YZ Plane). Distance between robots is 20 mm. Commute distance is 2 x 29.83 mm. The spreading
distance is 17.09 mm. Focus distance is 12.33 mm. This is the minimum distance which can be measured by the sensors.
41. 41
Figure 5.1.2. Side view of two robots (YZ Plane). Distance between robots is 30 mm. Commute distance is 2 x 39.49 mm. The spreading
distance is 22.65 mm. Focus distance is 11.55 mm. The lower boundary of the transmitted light starts to come to the ground instead of the
opposite robot surface.
42. 42
Figure 5.1.3. Side view of two robots (YZ Plane). Distance between robots is 60 mm. Commute distance is 2 x 68.90 mm. The spreading
distance is 48.88 mm. Focus distance is 1.3 mm. This is the optimum distance for the current sensor design. The design parameters for the
sensor pair are based on this distance. The focus of the reflected light passes through the IR receiver's center. As opposite robot moves away
from this distance, the focal point begins to move away from the IR receiver’s center again.
43. 43
Figure 5.1.4. Side view of two robots (YZ Plane). Distance between robots is 76 mm. Commute distance is 2 x 84.85 mm. The spreading
distance is 66.93 mm. Focus distance is 14.6 mm. At this distance, the focus of the reflected light begins to fall below the shell surface of the
light transmitter robot.
44. 44
Figure 5.1.5. Side view of two robots (YZ Plane). Distance between robots is 90 mm. Commute distance is 2 x 98.95 mm. The spreading
distance is 84.52 mm. Focus distance is 30.46 mm. This is considered as the farthest distance to be studied for this project.
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5.2. Behaviors of IR Sensors with respect to Different Distances on Top View (XZ
Plane)
Figure 5.2.1. Top view of two robots (XZ Plane). Distance between robots is 20 mm. The
spreading distance is 13.1 mm. Focus distance is 19.17 mm. This is the minimum distance which
can be measured by the sensors.
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Figure 5.2.2. Top view of two robots (XZ Plane). Distance between robots is 30 mm. The
spreading distance is 18.81 mm. Focus distance is 16.8 mm. At this distance, the focus of the
reflected light passes through the transmitter again
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Figure 5.2.3. Top view of two robots (XZ Plane). Distance between robots is 60 mm. The
spreading distance is 38.94 mm. Focus distance is 0 mm. This is the optimum distance for the
current sensor design. The design parameters for the sensor pair are based on this distance. The
focus of the reflected light passes through the IR receiver's center. As opposite robot moves away
from this distance, the focal point begins to move away from the IR receiver’s center again.
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Figure 5.2.4. Top view of two robots (XZ Plane). Distance between robots is 68 mm. The
spreading distance is 48.16 mm. Focus distance is 7.17 mm. After this distance, a certain portion
of the transmitted light starts to miss the surface of the opposite robot. The ratio of non-reflected
light to the total transmitted light is tabulated in the corresponding column (Non-Reflected Light
Ratio) in Table 10.
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Figure 5.2.5. Top view of two robots (XZ Plane). Distance between robots is 90 mm. Focus
distance is 33.79 mm. This is considered as the farthest distance to be studied for this project.