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.
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.
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.
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.
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.
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.
This document discusses swarm robotics and how robots communicate within a swarm. It defines swarm robotics as using small robots that communicate with each other to perform tasks. Swarms typically have a master robot that controls slave robots and guides them to complete assigned tasks. The robots communicate using transmitters and receivers that convert data between serial and parallel formats to transmit instructions and sensor readings between the master and slaves. Examples of applications for swarm robotics include industrial tasks, medical procedures, and military operations.
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.
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
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.
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.
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.
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.
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.
This document discusses swarm robotics and how robots communicate within a swarm. It defines swarm robotics as using small robots that communicate with each other to perform tasks. Swarms typically have a master robot that controls slave robots and guides them to complete assigned tasks. The robots communicate using transmitters and receivers that convert data between serial and parallel formats to transmit instructions and sensor readings between the master and slaves. Examples of applications for swarm robotics include industrial tasks, medical procedures, and military operations.
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.
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
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 :))
The document discusses swarm robots, including different types defined by size, communication range and topology. It describes how swarm robotics uses large numbers of simple robots that can collectively solve problems through local interactions. Examples of tasks include transportation, search and rescue. Challenges include scalability and performing physical tasks. Modeling approaches include microscopic and macroscopic, with the latter being more computationally efficient.
Swarm intelligence refers to the collective behavior that emerges from decentralized, self-organized systems, both natural and artificial. In nature, it can be seen in the ability of ant colonies and bird flocks to coordinate and complete tasks through simple local interactions between individuals. Artificial swarm intelligence systems are distributed systems of interacting autonomous agents that coordinate through self-organization to solve problems through cooperation and division of labor. Examples of algorithms inspired by swarm intelligence include ant colony optimization and particle swarm optimization.
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 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.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
This document provides an overview of swarm robotics. It defines swarm robotics as using groups of homogeneous robots that can accomplish tasks collectively or individually. Key points covered include swarm intelligence principles that inspire it, tasks involving individual or group behaviors, cooperation through interaction and communication, learning approaches, challenges like path planning and task allocation, and applications like searching, surveillance and oil spilling cleanup. Three case studies are presented involving collective energy homeostasis, multi-robot path planning for flying and driving robots, and self-organized flocking.
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.
This Presentation is for the final paper to professor's Andina Artificial Intelligent class @ Universidad Politecnic de Madrid, master in Telecommunication.
Vasile Vancea
vasile.vancea@alumnos.upm.es
mr.vasilevancea@yahoo.com
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
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 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.
The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.
This document discusses swarm intelligence, which is inspired by natural phenomena like bird flocking and ant foraging behavior. It describes how swarms exhibit complex behavior through individuals following simple rules without centralized control. Two swarm algorithms are covered: ant colony optimization, which is applied to the traveling salesman problem, and the bee algorithm. The document compares these algorithms and discusses applications of swarm intelligence concepts.
This document summarizes a lecture on multi-robot systems. It discusses why multi-robot systems are used, including for robustness, scalability, performance, and specialization. It covers reactive coordination algorithms inspired by ant colonies, which use indirect communication via pheromone trails. It also discusses deliberative coordination through the example of yacht racing crews. Key lessons are that multi-robot systems distribute sensing, computation and communication, and coordination algorithms are probabilistic approaches based on available capabilities.
This document describes a new approach for behavioral control of swarm robots using implicit communication. The key aspects of the proposed algorithm are:
1) Robots can operate in either a centralized or decentralized mode. In centralized mode, one robot acts as the leader and others follow. In decentralized mode, each robot plans its path individually.
2) Robots can switch between these modes depending on obstacles. If an obstacle disrupts communication between the leader and follower, they switch to decentralized mode.
3) Robots are equipped with sensors like IR sensors for obstacle avoidance and an IR seeker to navigate toward a goal. RF transceivers enable implicit communication between robots.
4) The algorithm was implemented using
This document discusses swarm intelligence and provides examples from nature. It describes how honey bees, wasps, and ants exhibit swarm behavior through cooperation, communication, and division of labor. It also discusses how natural phenomena like ant navigation using pheromone trails, bird flocking behavior, and ant colony optimization provide inspiration for swarm intelligence techniques.
MHead - Self-Organized Flocking in Mobile Robot SwarmsSamet Baykul
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.
A Corpus Based Analysis Of The Application Of Concluding Transition Signals ...Darian Pruitt
This document summarizes the past, present, and future of swarm robotics. It discusses how swarm robotics was inspired by natural swarms like ants and birds. Early work in the late 1980s first introduced the concepts of cellular robotics and swarm intelligence. Research has focused on emulating behaviors like foraging, flocking, and cooperation through simple robot rules and local interactions. Current work involves various simulation and real-world applications. The future of swarm robotics may include more complex collective behaviors and larger swarm sizes for tasks like search and rescue missions.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
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 :))
The document discusses swarm robots, including different types defined by size, communication range and topology. It describes how swarm robotics uses large numbers of simple robots that can collectively solve problems through local interactions. Examples of tasks include transportation, search and rescue. Challenges include scalability and performing physical tasks. Modeling approaches include microscopic and macroscopic, with the latter being more computationally efficient.
Swarm intelligence refers to the collective behavior that emerges from decentralized, self-organized systems, both natural and artificial. In nature, it can be seen in the ability of ant colonies and bird flocks to coordinate and complete tasks through simple local interactions between individuals. Artificial swarm intelligence systems are distributed systems of interacting autonomous agents that coordinate through self-organization to solve problems through cooperation and division of labor. Examples of algorithms inspired by swarm intelligence include ant colony optimization and particle swarm optimization.
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 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.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
This document provides an overview of swarm robotics. It defines swarm robotics as using groups of homogeneous robots that can accomplish tasks collectively or individually. Key points covered include swarm intelligence principles that inspire it, tasks involving individual or group behaviors, cooperation through interaction and communication, learning approaches, challenges like path planning and task allocation, and applications like searching, surveillance and oil spilling cleanup. Three case studies are presented involving collective energy homeostasis, multi-robot path planning for flying and driving robots, and self-organized flocking.
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.
This Presentation is for the final paper to professor's Andina Artificial Intelligent class @ Universidad Politecnic de Madrid, master in Telecommunication.
Vasile Vancea
vasile.vancea@alumnos.upm.es
mr.vasilevancea@yahoo.com
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
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 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.
The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.
This document discusses swarm intelligence, which is inspired by natural phenomena like bird flocking and ant foraging behavior. It describes how swarms exhibit complex behavior through individuals following simple rules without centralized control. Two swarm algorithms are covered: ant colony optimization, which is applied to the traveling salesman problem, and the bee algorithm. The document compares these algorithms and discusses applications of swarm intelligence concepts.
This document summarizes a lecture on multi-robot systems. It discusses why multi-robot systems are used, including for robustness, scalability, performance, and specialization. It covers reactive coordination algorithms inspired by ant colonies, which use indirect communication via pheromone trails. It also discusses deliberative coordination through the example of yacht racing crews. Key lessons are that multi-robot systems distribute sensing, computation and communication, and coordination algorithms are probabilistic approaches based on available capabilities.
This document describes a new approach for behavioral control of swarm robots using implicit communication. The key aspects of the proposed algorithm are:
1) Robots can operate in either a centralized or decentralized mode. In centralized mode, one robot acts as the leader and others follow. In decentralized mode, each robot plans its path individually.
2) Robots can switch between these modes depending on obstacles. If an obstacle disrupts communication between the leader and follower, they switch to decentralized mode.
3) Robots are equipped with sensors like IR sensors for obstacle avoidance and an IR seeker to navigate toward a goal. RF transceivers enable implicit communication between robots.
4) The algorithm was implemented using
This document discusses swarm intelligence and provides examples from nature. It describes how honey bees, wasps, and ants exhibit swarm behavior through cooperation, communication, and division of labor. It also discusses how natural phenomena like ant navigation using pheromone trails, bird flocking behavior, and ant colony optimization provide inspiration for swarm intelligence techniques.
MHead - Self-Organized Flocking in Mobile Robot SwarmsSamet Baykul
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.
A Corpus Based Analysis Of The Application Of Concluding Transition Signals ...Darian Pruitt
This document summarizes the past, present, and future of swarm robotics. It discusses how swarm robotics was inspired by natural swarms like ants and birds. Early work in the late 1980s first introduced the concepts of cellular robotics and swarm intelligence. Research has focused on emulating behaviors like foraging, flocking, and cooperation through simple robot rules and local interactions. Current work involves various simulation and real-world applications. The future of swarm robotics may include more complex collective behaviors and larger swarm sizes for tasks like search and rescue missions.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
This document summarizes research on ant colony optimization (ACO), a metaheuristic algorithm inspired by the foraging behavior of ants. It describes how real ant colonies use pheromone trails to efficiently find short paths between their nest and food sources through decentralized cooperation. The document then explains how ACO works by simulating artificial ants that probabilistically construct solutions and update pheromone values to guide future construction. Several standard ACO algorithms are outlined, including Ant System, Ant Colony System, Max-Min Ant System, and Rank-Based Ant System. Applications of ACO discussed include the traveling salesman problem.
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).
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 using swarm intelligence algorithms to control node movement in mobile medium ad hoc networks (M2ANETs). It proposes applying an ant colony optimization (ACO) approach where nodes move based on pheromone trails left by recent data forwarding activity. Simulation results showed this approach significantly improved delivery rates in sparse networks, increasing them by up to 50% by directing nodes to locations with recent activity. The document provides background on M2ANETs, self-organizing systems, swarm intelligence, and existing algorithms like particle swarm optimization and ant colony optimization that were the inspiration for the new approach.
This document summarizes an article from the International Journal of Electronics and Communication Engineering & Technology about biomimetic robots inspired by ants. It discusses how ants exhibit behaviors like discovering and exploiting food sources, and how robot teams could emulate these behaviors. The document then describes the key behaviors of ants, including foraging, recruiting, following pheromone trails, and retiring when unsuccessful. It proposes a structure for a team of three autonomous robots that would perform functions mimicking ants, including foraging randomly, exploiting food sources when found, recruiting other robots via communication, following recruited robots, and retiring when no food is found. Flow charts depict the process the ant-inspired robots would follow to search for and collect "food" targets.
This document summarizes an article from the International Journal of Electronics and Communication Engineering & Technology about biomimetic robots inspired by ants. It discusses how ants exhibit behaviors like discovering and exploiting food sources, and how robot teams could emulate these behaviors. The document then describes the key behaviors of ants, including how they sense food and communicate paths to it using pheromones. It proposes a structure for a team of ant-inspired robots that would work collaboratively to locate a target, with one robot acting as a leader to coordinate the others. Flow charts are provided to illustrate the process the biomimetic robots would follow to randomly search, detect a target, recruit others to help exploit it, and return to their nest.
Load balancing using ant colony in cloud computingijitcs
Ants are very small insects.They are capable to find food even they are complete blind. The ants lives in
their nest and their job is to search food while they get hungry. We are not interested in their living style,
such as how they live, how they sleep. But we are interested in how they search for food, and how they find
the shortest path. The technique for finding the shortest path are now applying in cloud computing. The Ant
Colony approach towards Cloud Computing gives better performance.
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile nodes… The problem remains the same: in order to get the best from the network, there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best.
The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Thus, they know where their nest is, and also their destination, without having a global view of the ground. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task.
The purpose of this project is to provide a clear understanting of the Ants-based algorithm, by giving a formal and comprehensive systematization of the subject. The simulation developed in Java will be a support of a deeper analysis of the factors of the algorithm, its potentialities and its limitations. Then the state-of-the-arts utilisation of this algorithm and its implementations in routing algorithms, mostly for mobile ad hoc networks, will be explained. Results of recent studies will be given and resume the current employments of this great algorithm inspired by the Nature.
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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
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An Improved Ant Colony System Algorithm for Solving Shortest Path Network Pro...
Communication in Swarm Robotics
1. COMMUNICATION IN SWARM ROBOTICS
Anuradhika Pilli!
Eastern Michigan University!
900 Oakwood St, Ypsilanti, MI - 48197; Ph: +1 325-280-4643!
e-mail: apilli@emich.edu!
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!
ABSTRACT
In this paper, the communication techniques in Swarm
Robotics using Ant Colony Optimization (ACO) are
discussed. This also includes the detailed explanation of the
topics: swarm robotics, Ant Colony Optimization and the
communication techniques in swarm robotics. Furthermore,
different studies and experiments done on swarm robotics by
few researchers are analyzed, compared and contrasted that
helped to determine the best technique for communication
between various swarm agents in swarm robotics. Also the
importance of this study is acknowledged by knowing few of
swarm robotics applications.
Keywords
Swarm Robotics, Stigmergy, Swarm agents, Ant Colony
Optimization (ACO), Eye-bots, Foot-bots, Foraging
behavior, Pheromone trail
1.INTRODUCTION
Since the past few decades communication between swarms
of insects has drawn interest of many. Not just
communication but also efficient means of navigation
through various broadcasting methods to obtain optimized
path has been a topic of great concern. Inspired by biological
swarms of insects, the concept of Swarm Robotics came into
existence.
The foraging framework plays a crucial role in Swarm
Robotics. Swarm robotics is a concept where group of
"robots" or "swarm agents" work together to accomplish a
complex task. Swarm robotics is a bio-inspired topic derived
from the well known algorithm, "Ant Colony Optimization
(ACO)." The concept of ACO is simple. Ants follow a
systematic and optimized method to find food and get it back
to their nests. Every ant upon finding food leaves a
pheromone trail (a volatile scented track) for other ants that
are wandering randomly in search of food, to find food. The
track becomes stronger when more ants pass on the same
trail, meaning the optimized path. This stigmergy [5] (a
mechanism of indirect coordination between agents or
actions) and foraging behavior [2] of ants introduced a
complete new dimension to Swarm Robotics.
For a successful foraging behavior [2], good communication
[5] between swarm agents is essential. The different methods
that can be employed in Swarm Robotics for communication
are telecommunication media like bluetooth, wireless LAN,
radio waves, light and other similar media transmissions
or communication via the environment(stigmergy). There has
always been an intense debate on which is the best
broadcasting method in Swarm Robotics, to find optimized
path.
In telecommunication, the information is exchanged between
every other swarm in various ways. Some of the popular
means of telecommunication are information exchange
via Bluetooth - this communication occurs when the agents
are in line of sight, communication using light sensors - the
agents detect light waves emitted by other robots and,
communication using radio waves - these waves enable
communication in any kind of environment. All these
telecommunication methods usually need an external robot or
computational device monitoring all the agents in order for
them to function effectively.
On the other hand, communication via environment is a
method where information transmission is done trying to
purely replicate the stigmergy [5] behavior of ants by
utilizing any kind of volatile chemical track as a source of
information. Using this method, the agents need not rely on
an external device for guidance which means they are self-
learning and also self-organizing which is the main basic idea
of Swarm Intelligence. Similar to ants that walk on ground
and communicate effortlessly on ground, stigmergy [5] in
swarm robotics is also limited to particular environment
types.
2.RELATED WORK STUDY
According to Fujisawa et al. (2014), telecommunication is
the general means of information transmission in Swarm
Robotics. Fujisawa et al. (2014) and Mayet et al. (2010)
claim that, the use of chemical compounds(stigmergy) in
foraging technique, that is, communication via environment
shows better results compared to the telecommunication
media. This paves a path to a controversial discussion as few
others like Ducatelle et al. (2011) strongly concur with the
idea of using telecommunication in Swarm Robotics.
2.1.Work of Fujisawa et al.
Fujisawa et al. (2014) and Mayet et al. (2010) support their
statement by demonstrating their experiments with robotic
swarms. In their experiment, Fujisawa et al. (2014) use
ethanol as a source of information which gets stronger when
many agents pass over it. This ethanol [3] also disappears
with time as it is volatile. Fujisawa et al. (2014) performed
three various tests on the robots.
Fujisawa et al. (2014) first performed a simulation test on
various parameters of scalibility like the presence or absence
of pheromone communication [3], number of robots and size
of the environment keeping all other parameters same in the
experiment. They found that performance is directly
proportional to number of robots and inversely proportional
to the environment size in both conditions, that is, presence
or absence of pheromone communication [3].
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2. The second test was a real setting in which Fujisawa et al.
used four sets of sensors: (1) touch sensors to detect food,
(2) phototransistors to detect nest, (3) alchol sensors to detect
pheromone trail and (4) an internal timer to check for
timeouts (timer starts when a robot loses its track and after 4
seconds if it does not find the track it changes its internal
state). The parameters such as size of the environment, food,
nest, and the duration of the experiment (20 mins) being the
same as in simulation experiment. The swarm size varied
from 1 - 10. The results were much similar to the simulation
experiment where the only difference is that the critical mass
behavior with pheromone communication [3] surpasses the
one without the use of pheromone communication [3].
The third test was the transport test. Setup was similar to the
real experimental setup. The start time with any number of
robots in the experiment was almost the same while the
execution time created a difference among them. The
performance also enhances with the use or pheromone
communication [3] rather than without it. Based on these
three tests, Fujisawa et al. (2014) conclude that
communication in swarms with pheromone is superior to the
communication without pheromone.
2.2.Work of Mayet et al.
Mayet et al. (2010) support the idea of communication via
environment by demonstrating their experiment in which
they use a single robot as a sample and also conduct a
simulation test. In their experiment, the robot consisted of red
and green LEDs, eight different infrared transiting and
receiving sensors, color CMOS camera and a phosphorescent
paint emitter [4]. The nest is red LED and the food source is
blue LED each of which is covered with transparent plastic.
A red light source had also been placed at a corner that acts
as “artificial sun”. This sun compass can be detected by the
robots in all directions.
Mayet et al. (2010) conducted a simulation test using this
setup. The red and green LED lights on robot show the state
of the robot. Red light indicates that the robot is in search of
food and the green indicates that it is going back to nest with
food. The camera detects the different colored LEDs based
on which the robot decides its direction of movement. The
test was conducted on different robot size with and without
using pheromone trail.
Initial state of robots is in red and upon finding the food
source it changes it state to green and search for the sun
compass to learn it’s location and depict the nest location
from there. Using pheromone trail - while coming back to the
nest with food, it releases the phosphorescent paint [4] which
disappears in time. This paint is identified by the UVLED
sensor built in the robot. The robot moves in the direction
with maximum intensity of paint. The results of this
simulation test conducted several times with varying robot
sizes showed that the task is performed efficiently with more
number of agents and using pheromone trail rather than
without using pheromone trail. In the real experiment,
Mayet et al. (2010) used a single robot with similar
functionalities as in the simulation test and the robot
navigates reliably between the food source and the nest.
2.3.Work of Ducatelle et al.
Ducatelle et al. (2011) present their point of view by
demonstrating their experiment in which they use
heterogenous robotic swarms. Every homogenous group has
its own work functionality. Initially, Ducatelle et al. (2011)
setup two different sub-swarms, mainly foot-bots [1]
and eye-bots [1] to investigate the local interactions and
mutual adaption among them. The foot-bots [1] are used for
navigation between the source and target in an indoor area.
The eye-bots [1] are flying robots that attach to
the ceiling. Eye-bots [1] first explore the indoor area and then
selecting a suitable place on the ceiling to sit on, from where
they could have a clear view of the whole arena so as to
direct the foot-bots [1] in navigation without any collision
with obstacles or other agents. The eye-bots [1] act as a
pheromone for the foot-bots [1]. The crucial task in their
study is how the eye-bots [1] need to be updated and how
could they give efficient direction to the foot-bots [1]. This
type of communication calls for synchronization and mutual
understanding between the different sub-swarms.
Ducatelle et al. (2011) further say that their system features
minimal information exchange, purely broadcast-based local
interactions based on short-range radio signals and simple
visual cues. As this approach is much similar to the
traditional telecommunication approach [5], Ducatelle et al.
(2011) say that the system is likely to be robust, adaptive
and scalable. Ducatelle et al. (2011) start off with the
description of the robots. Then they study the eye-bots [1].
Later they investigate the self-organized behavior of the
system for producing optimized results. And finally observe
how the eye-bots [1] adapt efficiently to the exact locations
on ceiling to give proper directions to the foot-bots [1]. The
experimental results show that the heterogenous swarms
work effectively and are quite robust as the synchronization
between them is flawless. As a result of this synchronization,
there are less collisions.
3.DISCUSSION AND CONCLUSION
The first two approaches are much similar. In both the cases
it had been proved that swarm communication is productive
with the use of pheromone trail rather than without using it.
Not only pheromone trail, but also the robot size was also
proportional to the productiveness. In the third approach,
communication was done using telecommunication, but with
the same idea of replicating the ACO algorithm. The eye-bots
[1] acted as a trail for the foot-bots [1]. With sterling
synchronization between foot-bots [1] and eye-bots [1], the
task can be fulfilled as outstandingly as in the first two
approaches.
Based on the three approaches, the use of chemical based
media or the pheromone communication [3] serves better.
This is for two reasons - Firstly, the agents are self-learning
which is the main basic concept of stigmergy [5] in Swarm
Intelligence, that is missing in the third approach that is
master and slave approach of eye-bots [1] and foot-bots [1]
mentioned above. The second reason is that, with the
increase in size of foot-bots [1] and the arena, the number of
eye-bots [1] should also be increased to work effectively and
the scope of each eye-bot may be limited. None-the-less both
the approaches serve fairly well in task accomplishment.
4.FUTURE STUDY
Basic drawback of pheromone based communication is that it
works only on ground of specific types and in
telecommunication it is the external control system. Not
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3. limiting it to the ground, researchers can take the inspiration
of jet planes and utilize the smoke concept that fades in a
while as the pheromone trail in pheromone communication
[3]. Even on water the oil leaking vessels can be a source of
inspiration. The agents can release some de-toxic chemicals
on or in the water that may serve as the pheromone trail. A
further advanced study in both approaches and a combination
of those could be of major enhancement in Swarm
Intelligence concept which would serve complex tasks much
easily than being discussed now.
5.WHY THIS STUDY IS IMPORTANT?
There are many applications of swarm robotics. One major
application is that it can be used in military forces where
much blood shed can be reduced. It is used in crowd
simulation, crowd control, path optimization and in
complexity optimization. All these applications are not
possible if there is no proper communication between the
swarms. Hence an effective communication technique is
needed to work wonders in accomplishing complex tasks.
6.REFERENCES
1. Ducatelle, F., Di Caro, G. A., Pinciroli, C.,
& Gambardella, L. M. (2011). Self-organized
cooperation between robotic swarms. In Swarm Intell
(5): (pp. 73–96). Austria: Springer Science + Business
Media.
2. Edelen, M. R. (2003). Thesis on Swarm intelligence and
stigmergy: Robotic implementation of foraging
behavior. http://drum.lib.umd.edu/bitstream/1903/107/1/
dissertation.pdf
3. Fujisawa, R., Dobata, S., Sugawara, K., & Matsuno, F.
(2014). Designing pheromone communication in swarm
robotics: Group foraging behavior mediated by chemical
substance. In Swarm Intell (8): (pp. 227–246).
Newyork: Springer Science + Business Media.
4. Mayet, R., Roberz, J., Schmickl, T., & Crailsheim, K.
(2010). Antbots: A feasible visual emulation of
pheromone trails for Swarm Robots. In Dorigo, M. et al.
(Eds.), Ants (pp. 84-94). Berlin Heidelberg: Springer-
Verlag.
5. Swarm communication in Jasmine Swarm robotic
platform. http://www.swarmrobot.org/
Communication.html
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