This is a full report of my project in Level 3 Term 1. The project was basically a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Flood Fill Algorithm will be used for finding the path between two allowed nodes. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another.
Autonomous laser guided vehicle for book deposition in a libraryPushkar Limaye
1. IIT Bombay students developed an autonomous laser-guided vehicle to automate book deposition in libraries.
2. The vehicle uses lasers, sensors and a microcontroller to navigate library aisles and deposit books in the correct locations based on barcodes scanned by a mobile phone.
3. Key components include an omni-directional base, lifting and pushing mechanisms to deposit books, and laser navigation assisted by a magnetometer for indoor navigation.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Navigation and Trajectory Control for Autonomous Robot/Vehicle (mechatronics)Mithun Chowdhury
The document is a presentation about navigation and trajectory control for autonomous vehicles. It was presented by two students from the University of Trento in Italy.
The presentation introduces mobile robot design considerations including the interrelation between tasks, environments, kinematic models, path/trajectory planning, and high-level and low-level control. It explains that the robot task and environment must be identified first and the kinematic model selected based on this. Path planning is then needed to generate admissible trajectories that satisfy the kinematic constraints. High-level control executes tasks and trajectories while low-level control handles velocity commands.
It also explains concepts like holonomic and non-holonomic constraints, accessibility spaces, and maneuvers
Modelling of walking humanoid robot with capability of floor detection and dy...ijfcstjournal
Most humanoid robots have highly complicated structure and design of robots that are very similar to
human is extremely difficult. In this paper, modelling of a general and comprehensive algorithm for control
of humanoid robots is presented using Colored Petri Nets. For keeping dynamic balance of the robot,
combination of Gyroscope and Accelerometer sensors are used in algorithm. Image processing is used to
identify two fundamental issues: first, detection of target or an object which robot must follow; second,
detecting surface of the ground so that walking robot could maintain its balance just like a human and
shows its best performance. Presented model gives high-level view of humanoid robot's operations.
DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM NITISH K
The document discusses the design and implementation of a path planning algorithm for a wheeled mobile robot in a known dynamic environment. It describes using an A* algorithm at a central control station to calculate the shortest path for the robot. If obstacles are detected, the robot's location and obstacle information is sent to update the environment map. The control station then recalculates the new shortest path for the robot. The system was tested experimentally and in simulation, showing it can effectively calculate the shortest path in a dynamic environment.
Autonomous laser guided vehicle for book deposition in a libraryPushkar Limaye
1. IIT Bombay students developed an autonomous laser-guided vehicle to automate book deposition in libraries.
2. The vehicle uses lasers, sensors and a microcontroller to navigate library aisles and deposit books in the correct locations based on barcodes scanned by a mobile phone.
3. Key components include an omni-directional base, lifting and pushing mechanisms to deposit books, and laser navigation assisted by a magnetometer for indoor navigation.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Navigation and Trajectory Control for Autonomous Robot/Vehicle (mechatronics)Mithun Chowdhury
The document is a presentation about navigation and trajectory control for autonomous vehicles. It was presented by two students from the University of Trento in Italy.
The presentation introduces mobile robot design considerations including the interrelation between tasks, environments, kinematic models, path/trajectory planning, and high-level and low-level control. It explains that the robot task and environment must be identified first and the kinematic model selected based on this. Path planning is then needed to generate admissible trajectories that satisfy the kinematic constraints. High-level control executes tasks and trajectories while low-level control handles velocity commands.
It also explains concepts like holonomic and non-holonomic constraints, accessibility spaces, and maneuvers
Modelling of walking humanoid robot with capability of floor detection and dy...ijfcstjournal
Most humanoid robots have highly complicated structure and design of robots that are very similar to
human is extremely difficult. In this paper, modelling of a general and comprehensive algorithm for control
of humanoid robots is presented using Colored Petri Nets. For keeping dynamic balance of the robot,
combination of Gyroscope and Accelerometer sensors are used in algorithm. Image processing is used to
identify two fundamental issues: first, detection of target or an object which robot must follow; second,
detecting surface of the ground so that walking robot could maintain its balance just like a human and
shows its best performance. Presented model gives high-level view of humanoid robot's operations.
DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM NITISH K
The document discusses the design and implementation of a path planning algorithm for a wheeled mobile robot in a known dynamic environment. It describes using an A* algorithm at a central control station to calculate the shortest path for the robot. If obstacles are detected, the robot's location and obstacle information is sent to update the environment map. The control station then recalculates the new shortest path for the robot. The system was tested experimentally and in simulation, showing it can effectively calculate the shortest path in a dynamic environment.
This document discusses different path planning techniques for robot motion. It describes how configuration space represents all possible robot positions and orientations. Combinatorial planning methods decompose this space into cells to find obstacle-free paths, while sampling-based planning uses techniques like rapidly exploring random trees to quickly explore the space and find solutions without fully mapping obstacles. The document provides examples of how these methods are applied to problems in 2D and 3D worlds.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Automatic vision based inspection of railway trackeSAT Journals
Abstract Currently, most of railway track inspections are manually conducted by railroad track inspectors. Practically, it is not possible to inspect the thousand of miles of railway track by trained human inspector. This inspection takes too much time to inspect the defected railway track and then inform to the railway authority people. In this way it may lead to disaster. Hence to avoid delay and improve the accuracy, our propose system will automatically inspect the railway track by using vision based method and vibration based method. This method proposes continuous monitoring and assessment of the condition of the rail tracks which prevent major disasters. Our proposed system will inspect the rail track component such as missing bolts, tie plates, anchors etc by using vision based method and simultaneously do the calibration of railway track by using vibration based method. The system provides real-time monitoring and structural condition for railway track using vision based method and calibration to search the fault location on the track. Inspections include detecting defects on tracks, missing bolts, anchor, tie plate and clips etc. In vision based method camera we will use to capture the images or videos. In vibration based method some sensors we will use to detect the vibrations on the railway track. We will extract the signal from 2-D. Keywords: Railway track inspection, Vision based and vibration based method, Image processing, Data acquisition.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract Traffic congestion on city road networks is one of the main issues to be addressed by today’s traffic management schemes. The frequent traffic jams at major junctions call for an efficient traffic management system in place. The image sequences from a camera are analyzed using edge detection technique, object counting method and queue length estimation to obtain the most efficient technique. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. The paper also proposes to implement a real-time emergency vehicle detection system. In case an emergency vehicle is detected, the lane is given priority over all the others. Using image-processing operations to calculate traffic density is cost effective as cameras are cheaper and affordable devices compared to any other devices such as sensors. Keywords: Edge detection, Object counting, vehicle queue length, traffic management, image processing.
This document discusses using MATLAB's Robotics Toolbox to simulate a robotic arm with up to 6 degrees of freedom. The toolbox allows users to model different robotic arm configurations using revolute or prismatic joints. It covers the technical background of forward and inverse kinematics analysis. Some advantages of the toolbox are that the code is mature and provides a basis for comparison. The future scope is that data from the toolbox can be used with other MATLAB tools like neural networks. Results show the end effector positions can be found for different joint angles.
Abstract: This Project describes a visual sensor system used in the field of robotics for identification and tracking of the colored object. The program is designed to capture an Object through a Camera. It describes image capturing and processing techniques, followed by an introduction to actual robotic application to trace the Object using the serial COM port of the PC. The whole system of making a robot to follow an object can be divided into four blocks: image acquisition, processing of image, decision-making and motion control.
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
1) The document describes a proposed note to coin converter machine that would detect fake notes and convert real notes into coins for users.
2) The machine would use image processing techniques like HSI color modeling and thresholding in MATLAB to identify the denomination of inserted notes.
3) If a note is determined to be real, the equivalent number and type of coins would be dispensed based on the note's value. However, if a note is identified as fake, it would be ejected without providing any coins.
Trajectory reconstruction for robot programming by demonstration IJECEIAES
The reproduction of hand movements by a robot remains difficult and conventional learning methods do not allow us to faithfully recreate these movements because it is very difficult when the number of crossing points is very large. Programming by Demonstration gives a better opportunity for solving this problem by tracking the user’s movements with a motion capture system and creating a robotic program to reproduce the performed tasks. This paper presents a Programming by Demonstration system in a trajectory level for the reproduction of hand/tool movement by a manipulator robot; this was realized by tracking the user’s movement with the ArToolkit and reconstructing the trajectories by using the constrained cubic spline. The results obtained with the constrained cubic spline were compared with cubic spline interpolation. Finally the obtained trajectories have been simulated in a virtual environment on the Puma 600 robot.
The main objective of traffic surveillance system is to reduce the risk caused by accident. Many papers
published were concerned only about the vehicle detection during daytime. But we proposed a method to
detect the vehicle during night time. The main objective of our paper is to count the vehicles in night
time traffic scenes. It consists of three phases, they are vehicle’s headlight segmentation, tracking and
pairing. We know that in night time, only visible thing will be the headlight of the vehicle therefore we
are counting the vehicle based on the headlight information. Initially the headlights of a vehicle is
segmented based on the analysis of headlight size, location and area. Then tracked via a tracking
procedure designed to detect vehicle and then headlights are paired using the connected component
labeling. Based on the pairing result we can count the vehicles. Here we are using fuzzy hybrid
information inference mechanism for error compensation. Here by using fuzzy logic we can generate the
rules based on the size, color and position. In particular we are generating the rules based on the
distance between the headlights of a vehicle.Thereby we can improve the accuracy while counting the
vehicle.
This document summarizes a novel predictive seam tracking method for high-precision robotic laser welding using feedforward compensation. The method uses an industrial robot with off-the-shelf sensors to track both linear and nonlinear seams at speeds up to 100 mm/s with a tracking error of less than 0.1 mm. It introduces an architecture that measures residual errors during non-welding passes and compensates for them during an actual welding pass to improve tracking accuracy for demanding laser welding applications.
A Study on Single Camera Based ANPR System for Improvement of Vehicle Number ...journal ijrtem
This document summarizes a study on a single camera-based automatic number plate recognition (ANPR) system to recognize vehicle license plates on multi-lane roads. It proposes a character extraction algorithm using connected vertical and horizontal edge segments to improve recognition rates. The algorithm detects character edge patterns, extracts components by cumulatively labeling edges, and enhances images through contrast adaptive binarization. An ANPR system was installed on a 3-lane test road and achieved a detection rate of 84.6%, though some errors occurred with specific vehicle models or character differences. Further research is needed to handle low-visibility conditions and improve accuracy.
Pedestrian-traffic Logging Unit with Tailgating DetectionUsingRange Image SensorIDES Editor
This paper proposes a method for logging people
whichpass through a gateway in buildings or regions. The
proposed method detectsunexpected passing called tailgating.
The tailgating means that anon-identified person tries to enter
or leave a room by tagging after anotheridentified person.
The tailgating person does not appear on the log recordedby
conventional identification systems. The proposed method
logs the passingin a person-unit by using cameras and a range
image sensor. Firstly, thenumber of people in front of the card
reader is counted by the range imagesensor. Secondly, the
camera image taken at the same time as the identificationis
separated individually based on the projected range image.
Lastly,the passing is logged in a person-unit. The tailgating
person is logged withthe individual camera image and the ID
of the inviter. Experimental resultshave demonstrated that
the prototype of the proposed method can obtainthe log of the
passing including tailgating people.
Driverless Metro Train Shuttle between the Stations using LabVIEWijtsrd
The aim of this paper is to illustrate an improvement in the existing technology used in metro train movements. This train is equipped with a controller and an IR Object sensor that enables the automatic stopping of the train from one station to another station. This paper presents the development process of a prototype for a driverless train implemented using a RASPBERRY PI controller. Simulation for the systems circuits is done with LabVIEW software. The hardware circuit is interfaced with actuators and sensors for automation purposes using LabVIEW. A hardware comprised of IR sensor, RASPBERRI PI areassembled in a prototype train. A LabVIEW CODE is used for programming the controller. A Smoke sensor is also interfaced to detect any smoke or gas present in the train. Manoj Kumar M | Hemavathi.R | H. Prasanna Kumar"Driverless Metro Train Shuttle between the Stations using LabVIEW" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7088.pdf http://www.ijtsrd.com/engineering/automotive-engineering/7088/driverless-metro-train-shuttle-between-the-stations-using-labview/manoj-kumar-m
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
The document describes a proposal for a line maze solver robot project. It includes an introduction to line mazes, the objectives of the project to build an autonomous robot that can solve a line maze, and the key components and methodology. The robot will use 6 light sensors to detect the black line on a white surface and make decisions at intersections. It will use an Arduino microcontroller to process sensor input and control the motors. The first run will record wrong turns to avoid on the second run when it can solve the maze quickly.
IRJET - Autonomous Eviscerating BOT using ANT Colony OptimizationIRJET Journal
This document proposes an autonomous cleaning bot system that uses two bots to clean floors efficiently. The bots use Ant Colony Optimization (ACO) algorithms and communicate wirelessly to determine optimal, time-saving paths without overlapping work. Each bot has sensors, a controller, motors and a transceiver. They sense obstacles, communicate paths to each other, and move autonomously using ACO principles, like ants leaving pheromone trails, to clean commercial spaces like malls in a coordinated way without human supervision.
This document describes an automatic robot system used to transport goods within industries using RF modules. The proposed system uses two robots, Robot A and Robot B, which receive destination coordinates from an RF module to find the shortest path. Robot A is given higher priority than Robot B to avoid collisions at junctions. The robots can follow lines, traverse grids, pick up objects, and avoid collisions through the use of sensors and a microcontroller. The RF module allows the robots to communicate and efficiently transport goods while minimizing time, power usage, and collisions between the robots.
This document discusses different path planning techniques for robot motion. It describes how configuration space represents all possible robot positions and orientations. Combinatorial planning methods decompose this space into cells to find obstacle-free paths, while sampling-based planning uses techniques like rapidly exploring random trees to quickly explore the space and find solutions without fully mapping obstacles. The document provides examples of how these methods are applied to problems in 2D and 3D worlds.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Automatic vision based inspection of railway trackeSAT Journals
Abstract Currently, most of railway track inspections are manually conducted by railroad track inspectors. Practically, it is not possible to inspect the thousand of miles of railway track by trained human inspector. This inspection takes too much time to inspect the defected railway track and then inform to the railway authority people. In this way it may lead to disaster. Hence to avoid delay and improve the accuracy, our propose system will automatically inspect the railway track by using vision based method and vibration based method. This method proposes continuous monitoring and assessment of the condition of the rail tracks which prevent major disasters. Our proposed system will inspect the rail track component such as missing bolts, tie plates, anchors etc by using vision based method and simultaneously do the calibration of railway track by using vibration based method. The system provides real-time monitoring and structural condition for railway track using vision based method and calibration to search the fault location on the track. Inspections include detecting defects on tracks, missing bolts, anchor, tie plate and clips etc. In vision based method camera we will use to capture the images or videos. In vibration based method some sensors we will use to detect the vibrations on the railway track. We will extract the signal from 2-D. Keywords: Railway track inspection, Vision based and vibration based method, Image processing, Data acquisition.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract Traffic congestion on city road networks is one of the main issues to be addressed by today’s traffic management schemes. The frequent traffic jams at major junctions call for an efficient traffic management system in place. The image sequences from a camera are analyzed using edge detection technique, object counting method and queue length estimation to obtain the most efficient technique. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. The paper also proposes to implement a real-time emergency vehicle detection system. In case an emergency vehicle is detected, the lane is given priority over all the others. Using image-processing operations to calculate traffic density is cost effective as cameras are cheaper and affordable devices compared to any other devices such as sensors. Keywords: Edge detection, Object counting, vehicle queue length, traffic management, image processing.
This document discusses using MATLAB's Robotics Toolbox to simulate a robotic arm with up to 6 degrees of freedom. The toolbox allows users to model different robotic arm configurations using revolute or prismatic joints. It covers the technical background of forward and inverse kinematics analysis. Some advantages of the toolbox are that the code is mature and provides a basis for comparison. The future scope is that data from the toolbox can be used with other MATLAB tools like neural networks. Results show the end effector positions can be found for different joint angles.
Abstract: This Project describes a visual sensor system used in the field of robotics for identification and tracking of the colored object. The program is designed to capture an Object through a Camera. It describes image capturing and processing techniques, followed by an introduction to actual robotic application to trace the Object using the serial COM port of the PC. The whole system of making a robot to follow an object can be divided into four blocks: image acquisition, processing of image, decision-making and motion control.
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
1) The document describes a proposed note to coin converter machine that would detect fake notes and convert real notes into coins for users.
2) The machine would use image processing techniques like HSI color modeling and thresholding in MATLAB to identify the denomination of inserted notes.
3) If a note is determined to be real, the equivalent number and type of coins would be dispensed based on the note's value. However, if a note is identified as fake, it would be ejected without providing any coins.
Trajectory reconstruction for robot programming by demonstration IJECEIAES
The reproduction of hand movements by a robot remains difficult and conventional learning methods do not allow us to faithfully recreate these movements because it is very difficult when the number of crossing points is very large. Programming by Demonstration gives a better opportunity for solving this problem by tracking the user’s movements with a motion capture system and creating a robotic program to reproduce the performed tasks. This paper presents a Programming by Demonstration system in a trajectory level for the reproduction of hand/tool movement by a manipulator robot; this was realized by tracking the user’s movement with the ArToolkit and reconstructing the trajectories by using the constrained cubic spline. The results obtained with the constrained cubic spline were compared with cubic spline interpolation. Finally the obtained trajectories have been simulated in a virtual environment on the Puma 600 robot.
The main objective of traffic surveillance system is to reduce the risk caused by accident. Many papers
published were concerned only about the vehicle detection during daytime. But we proposed a method to
detect the vehicle during night time. The main objective of our paper is to count the vehicles in night
time traffic scenes. It consists of three phases, they are vehicle’s headlight segmentation, tracking and
pairing. We know that in night time, only visible thing will be the headlight of the vehicle therefore we
are counting the vehicle based on the headlight information. Initially the headlights of a vehicle is
segmented based on the analysis of headlight size, location and area. Then tracked via a tracking
procedure designed to detect vehicle and then headlights are paired using the connected component
labeling. Based on the pairing result we can count the vehicles. Here we are using fuzzy hybrid
information inference mechanism for error compensation. Here by using fuzzy logic we can generate the
rules based on the size, color and position. In particular we are generating the rules based on the
distance between the headlights of a vehicle.Thereby we can improve the accuracy while counting the
vehicle.
This document summarizes a novel predictive seam tracking method for high-precision robotic laser welding using feedforward compensation. The method uses an industrial robot with off-the-shelf sensors to track both linear and nonlinear seams at speeds up to 100 mm/s with a tracking error of less than 0.1 mm. It introduces an architecture that measures residual errors during non-welding passes and compensates for them during an actual welding pass to improve tracking accuracy for demanding laser welding applications.
A Study on Single Camera Based ANPR System for Improvement of Vehicle Number ...journal ijrtem
This document summarizes a study on a single camera-based automatic number plate recognition (ANPR) system to recognize vehicle license plates on multi-lane roads. It proposes a character extraction algorithm using connected vertical and horizontal edge segments to improve recognition rates. The algorithm detects character edge patterns, extracts components by cumulatively labeling edges, and enhances images through contrast adaptive binarization. An ANPR system was installed on a 3-lane test road and achieved a detection rate of 84.6%, though some errors occurred with specific vehicle models or character differences. Further research is needed to handle low-visibility conditions and improve accuracy.
Pedestrian-traffic Logging Unit with Tailgating DetectionUsingRange Image SensorIDES Editor
This paper proposes a method for logging people
whichpass through a gateway in buildings or regions. The
proposed method detectsunexpected passing called tailgating.
The tailgating means that anon-identified person tries to enter
or leave a room by tagging after anotheridentified person.
The tailgating person does not appear on the log recordedby
conventional identification systems. The proposed method
logs the passingin a person-unit by using cameras and a range
image sensor. Firstly, thenumber of people in front of the card
reader is counted by the range imagesensor. Secondly, the
camera image taken at the same time as the identificationis
separated individually based on the projected range image.
Lastly,the passing is logged in a person-unit. The tailgating
person is logged withthe individual camera image and the ID
of the inviter. Experimental resultshave demonstrated that
the prototype of the proposed method can obtainthe log of the
passing including tailgating people.
Driverless Metro Train Shuttle between the Stations using LabVIEWijtsrd
The aim of this paper is to illustrate an improvement in the existing technology used in metro train movements. This train is equipped with a controller and an IR Object sensor that enables the automatic stopping of the train from one station to another station. This paper presents the development process of a prototype for a driverless train implemented using a RASPBERRY PI controller. Simulation for the systems circuits is done with LabVIEW software. The hardware circuit is interfaced with actuators and sensors for automation purposes using LabVIEW. A hardware comprised of IR sensor, RASPBERRI PI areassembled in a prototype train. A LabVIEW CODE is used for programming the controller. A Smoke sensor is also interfaced to detect any smoke or gas present in the train. Manoj Kumar M | Hemavathi.R | H. Prasanna Kumar"Driverless Metro Train Shuttle between the Stations using LabVIEW" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7088.pdf http://www.ijtsrd.com/engineering/automotive-engineering/7088/driverless-metro-train-shuttle-between-the-stations-using-labview/manoj-kumar-m
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
The document describes a proposal for a line maze solver robot project. It includes an introduction to line mazes, the objectives of the project to build an autonomous robot that can solve a line maze, and the key components and methodology. The robot will use 6 light sensors to detect the black line on a white surface and make decisions at intersections. It will use an Arduino microcontroller to process sensor input and control the motors. The first run will record wrong turns to avoid on the second run when it can solve the maze quickly.
IRJET - Autonomous Eviscerating BOT using ANT Colony OptimizationIRJET Journal
This document proposes an autonomous cleaning bot system that uses two bots to clean floors efficiently. The bots use Ant Colony Optimization (ACO) algorithms and communicate wirelessly to determine optimal, time-saving paths without overlapping work. Each bot has sensors, a controller, motors and a transceiver. They sense obstacles, communicate paths to each other, and move autonomously using ACO principles, like ants leaving pheromone trails, to clean commercial spaces like malls in a coordinated way without human supervision.
This document describes an automatic robot system used to transport goods within industries using RF modules. The proposed system uses two robots, Robot A and Robot B, which receive destination coordinates from an RF module to find the shortest path. Robot A is given higher priority than Robot B to avoid collisions at junctions. The robots can follow lines, traverse grids, pick up objects, and avoid collisions through the use of sensors and a microcontroller. The RF module allows the robots to communicate and efficiently transport goods while minimizing time, power usage, and collisions between the robots.
Automatic Robot System In Industries Using Rf Module iosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document describes a line following robot project created by students at the Shri Govindram Seksaria Institute of Technology and Science Indore. The robot uses 3 IR sensor pairs and 2 motors to follow a black line on a white surface. It works by using the IR sensors to detect the line and send signals to the motor control circuitry, which instructs the motors to move the robot forward or turn as needed to stay on the line. The document discusses the components, working model, block diagram, applications and conclusions of the project. It proposes areas for future work, such as using a microcontroller and color sensors to add obstacle avoidance and other capabilities to the robot.
Implementation of pid control to reduce wobbling in a line following roboteSAT Journals
This document summarizes the implementation of a PID control system on a line following robot to reduce wobbling and improve tracking of the line. It describes the components of the robot including sensors, microcontroller, motors and power source. It discusses line following without PID which resulted in large deviations and inability to follow at high speeds. The document then provides details on how PID control was implemented, including definitions of target position, measured position, error, and the proportional, integral and derivative components. It explains how these factors were coded into the microcontroller to calculate motor speeds. The results showed much smoother line following with minimal wobbling even at high speeds compared to without PID control.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET-Fuzzy Logic Based Path Navigation for Robot using MatlabIRJET Journal
This document describes a fuzzy logic-based path navigation system for a robot using MATLAB. The system uses infrared sensors to detect obstacles and a fuzzy logic controller with four input and two output variables to navigate around static obstacles. If moving obstacles are detected, the system generates a trajectory prediction table to plan a new path to avoid collisions. The system was tested in a simulator environment with static and moving obstacles and was able to successfully navigate to a target location while avoiding obstacles. The fuzzy logic controller provided an effective way to control the robot's direction and generate smooth motion to reach the target safely.
The document describes the design of a line following robot. It will use eight optical sensors spaced half an inch apart to follow black lines on a white surface. A two-wheeled configuration propelled by DC motors is selected. The robot will be tested by passing it over sample tracks to evaluate its ability to navigate turns and intersections. Its performance on tracks of varying difficulty will demonstrate how the design can accomplish autonomous navigation and complete reconnaissance missions.
This document describes a graduate project submitted by Zainab Falaih Hasan Ulla Ahmed Ouda for the degree of Bachelor of Automated Manufacturing Engineering. The project involves designing and building a prototype of a black line tracking robot. The robot uses sensors and a microcontroller to follow a black line on a white surface and maneuver turns. It is intended to function autonomously within an automated factory environment. The document provides background on the project, acknowledges those involved in advising and supporting the work, and outlines the various chapters that will comprise the project report, including the robot design, hardware components, implementation details, results, and proposals for future work.
Design and implementation of path planning algorithm for wheeled mobile robot...eSAT Journals
Abstract Path planning in mobile robots must ensure optimality of the path. The optimality achieved may be in path, time, energy consumed etc. Path planning in robots also depends on the environment in which it operates like, static or dynamic, known or unknown etc. Global path planning using A* algorithm and genetic algorithm is investigated in this paper. A known dynamic environment, in which a control station will compute the shortest path and communicate to the mobile robot and the mobile robot, will traverse through this path to reach the goal. The control station will keep track of the path traversed by the robot. The mobile robot navigates through the shortest path and if the robot detects any obstacle in the destined path, the mobile robot will update the information about the environment and this information together with the current location will be communicated to the control station. Then the control station, with the updated map of the environment and new starting location and destination recalculates the new shortest path, if any, and will communicate to the mobile robot so that it can reach the destination. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that the technique effectively calculates the shortest path in known dynamic environment and allows the robot to quickly accomplish the mission.
Design and implementation of path planning algorithm for wheeled mobile robot...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highwa...cscpconf
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This document describes the design and development of an RF-based spy robot. The robot is designed to quietly enter enemy areas and transmit information such as video footage wirelessly via a mounted camera. It uses color sensors to change its own color camouflage and avoid detection. The robot's movement is controlled remotely via an RF transmitter and receiver. Additional features include a gas sensor to detect poisonous gases. The robot is intended to reduce human casualties by substituting for soldiers in dangerous situations like wars or threats in public places.
A line follower robot is designed to follow a predetermined path marked by a physical line or other markers. Various sensing schemes can detect these markers, ranging from simple low-cost line sensors to complex vision systems. Line follower robots are commonly used in manufacturing plants to move along specified paths and pick up and place components. They work by using sensors to detect the line path and feedback mechanisms to stay on course while correcting deviations.
Design and Construction of Line Following Robot using Arduinoijtsrd
Line following robot is an autonomous vehicle which detect black line to move over the white surface or bright surface. In this paper, the line following robot is constructed by using Arduino nano microcontroller as a main component and consists of three infrared IR sensors, four simple DC motors, four wheels and a PCB frame of robot chassis. The infrared sensors are used to sense the black line on white surface. When the infrared signal falls on the white surface, it gets reflected and it falls on the black surface, it is not reflected. In this system, four simple DC motors attached with four wheels are used to move the robot cars direction that is left, right and forward. The Arduino nano is used as a controller to control the speed of DC motors from the L2953D driver circuit. Khin Khin Saw | Lae Yin Mon ""Design and Construction of Line Following Robot using Arduino"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23977.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23977/design-and-construction-of-line-following-robot-using-arduino/khin-khin-saw
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PC-based mobile robot navigation sytemANKIT SURATI
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This document summarizes a research paper on a shortest path follower robot. It describes the design of a line following robot that can detect the shortest path using IR sensors to follow a black line on a white surface. The robot uses an Arduino microcontroller connected to IR sensors and motors to determine the optimal path between a starting point and destination. It aims to solve the single source shortest path problem by identifying obstacles and navigating efficiently. The system architecture includes IR sensors to detect the line, motors to move the robot, and an Arduino board to process sensor readings and control the motors to follow the shortest route.
This document describes a proposed smart bus transportation system that uses various sensors and IoT devices to provide real-time passenger information. The system would use an FPGA controller, IR sensors to count passengers, a GPS module to track bus location, and an IoT module to send passenger and location data to an app. This would allow passengers to see seat availability and estimated time of arrival for buses, helping them make better travel decisions. The document reviews related works using IoT for transportation systems and provides a block diagram of the key components of the proposed smart bus system.
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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Artificial intelligence (AI) | Definitio
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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.
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Grid Based Autonomous Navigator
1. BANGLADESH UNIVERSITY OF ENGINEERING & TECHNOLOGY
Course No. : ME 362
Instrumentation and Measurement Sessional
Department of Mechanical Engineering
Grid-Based Navigation for Autonomous, Mobile Robots and Finding the
Conditional Shortest Path in an Unknown Environment
Accomplished by:
Sayeed Mohammed (1110067)
Md. Rakibul Islam (1110075)
Syeda Noor -E- Lamia (1110083)
3. ACKNOWLEDGEMENTS
First we like to thank Almighty for the successful submission of our project within time. We
would like to express our gratitude to our teacher Mr. Kazi Arafat Rahman, K.M Rafidh
Hasan and Anup Saha. From Department of Mechanical Engineering, BUET, for their
valuable suggestion about mechanical design and selection of electrical components. Finally
we want to thank our course teacher, Dr. M. A. Rashid Sarkar (Professor) from Department
of Mechanical Engineering, BUET. We are really grateful to those persons who have
supported us to complete our project.
4. ABSTRACT
A self-driven vehicle capable of localizing itself in a grid and planning a path between two
nodes. It can avoid particular nodes and plan path between two allowed nodes. Flood Fill
Algorithm will be used for finding the path between two allowed nodes. The vehicle is also
capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of
a self-driven vehicle capable of transporting passengers and it can also be used in industries
to transfer different items from one place to another.
KEYWORDS
Flood Fill, Microcontroller, Path Planning, Robust, PID, navigation, gird solving.
5. Index
Topic Page No.
1. Introduction……………………………………………………………………… 1
2. Grid description…………………………………………………………………. 2
3. Solving algorithm
3.1 Flood fill…………………………………………………………………... 3
3.2 Line following algorithm
3.2.1 Basic line following
algorithm…………………………………………………………...
4
3.2.2 PID line following
algorithm…………………………………………………………...
6
4. Components
4.1 Electrical components…………………………………………………….. 9
4.2 Mechanical components…………………………………………………... 12
5. Circuit design
5.1 Motherboard………………………………………………………………. 13
5.2 DC motor drive……………………………………………………………. 14
5.3 Line sensor interfacing……………………………………………………. 14
5.4 Overall electrical system………………………………………………….. 15
6. System prototype………………………………………………………………... 16
7. Future work……………………………………………………………………… 16
8. Conclusion………………………………………………………………………. 16
9. References……………………………………………………………………….. 17
10 Appendix………………………………………………………………………… 19
6. List of Figures
Figure name Page
no.
1. Grid……………………………………………………………………………… 2
2. LFR Principle using IR sensor at the side…………………………………….... 4
3. LFR Principle using IR sensor at the middle …………………………………... 5
4. Robot track before PID tuning………………………………………………….. 6
5. Robot Track after PID tuning …………………………………………………... 7
6. Arduino Mega 2650……………………………………………………………. 9
7. L298N Motor Drive…………………………………………………………….. 9
8. DC Gear motor………………………………………………………………….. 10
9. Buck module……………………………………………………………………. 10
10. Booster module…………………………………………………………………. 10
11. Battery………………………………………………………………………....... 11
12. IR sensor………………………………………………………………………… 11
13. Arduino Line following shield. ………………………………………………… 11
14. Al-Co alloy steel………………………………………………………………… 12
15. Caster ball……………………………………………………………………….. 12
16. Wheels…………………………………………………………………………... 12
17. Motherboard…………………………………………………………………….. 13
18. Schematics Of Motherboard…………………………………………………….. 13
19. Circuit connection of Motors …………………………………………………... 14
20. IR Array…………………………………………………………………………. 15
21. Principle of Sensor Array……………………………………………………….. 15
22. Overall Electrical Systems……………………………………………………… 15
23. Design of Robot………………………………………………………………… 16
7. 1
1. INTRODUCTION
A key ability needed by an autonomous, mobile robot is the possibility to navigate through
the space. The problem can basically be decomposed into positioning and path planning.
Especially if the robot is severely resource-constrained, simple schemes are favorable to
elaborated algorithms. Rather simple sensors and actuators as well as a limited computing
platform also demand simple, robust techniques due to inaccuracy and the lack of resources.
Autonomous navigation of mobile robot has been appeared in literature many times. Our
robot is an autonomous mobile robot capable of exploring 2D world, finding shortest path
between source and destination. It can also transfer objects of different sizes and shapes from
one place to another.
The report is structured in following way: At first the nature of the grid and programming
technique required to solve it is discussed and in the next section the mechanical construction
is detailed.
8. 2
2. GRID DESCRIPTION
The grid used as a reference is a 5×5 square grid having 300mm×300mm inner dimension of
40mm thick white lines on a black surface. The lines are spaced equally. There are nodes at
the intersection of white lines at some places and the nodes are black squares of
40mm×40mm dimension (Fig.1).
Fig 1: Grid
9. 3
3. SOLVING ALGORITHM
The grid is represented in a 2D system. Starting node is assumed as the origin. During the
first scan of the grid, the robot comes across every crossing in the grid and finds out whether
there is a white node in it. All the four directions are marked in which the bot can move so as
to know to where the bot is going to move next. Referring the 2D system in geological
directions NEWS, we will take NORTH or +y axis as 1 and the following directions as 2, 3
and 4. In this way the robot can be guided precisely to the destination node. In the code a
direction counter is kept for this purpose. The value of the counter is changed whenever
needed..
3.1. Flood Fill
The flood fill algorithm takes three parameters: a start node, a target color, and a replacement
color. The algorithm looks for all nodes in the array which are connected to the start node by
a path of the target color, and changes them to the replacement color. There are many ways in
which the flood-fill algorithm can be structured, but they all make use of a queue or stack
data structure, explicitly or implicitly.
One implicitly stack-based (recursive) flood-fill implementation (for a two-dimensional array) goes as
follows:
Flood-fill (node, target-color, replacement-color):
1. If target-color is equal to replacement-color, return.
2. If the color of node is not equal to target-color, return.
3. Set the color of node to replacement-color.
4. Perform Flood-fill (one step to the west of node, target-color,
replacement-color).
Perform Flood-fill (one step to the east of node, target-color,
replacement-color).
Perform Flood-fill (one step to the north of node, target-color,
replacement-color).
Perform Flood-fill (one step to the south of node, target-color,
replacement-color).
5. Return.
10. 4
3.2. Line Following Algorithm
3.2.1. Basic Line Following Algorithm
The algorithm is the one thing that determines the performance of your robot more than
anything else. The most basic algorithm is the one which uses only one sensor. The sensor is
placed in a position that is a little off centered to one of the sides, say right. When the sensor
is detects no line the robot moves to the left and when the sensor detects the line the robot
moves to the right. A robot with this algorithm would follow the line like shown in the
picture below
Fig 3: LFR Principle using IR sensor at the side
The drawback of this method is that the line following is not smooth. The robot keeps
wavering left and right on the track, wasting battery power and time. A modification to this
method is to add sensors on both sides of the robot and place them such that they just sense
11. 5
the line on either side. And the algorithm would be to move forward if both the sensors sense
the line or to move left if only the left sensor senses the line and move right if only the right
sensor senses the line. A robot with this algorithm would follow the line like shown in the
picture below
Fig 4: LFR Principle using IR sensor at the middle
This algorithm is faster than the previous algorithm but the robot will still wobble about the
line and may not be fast enough. A much better algorithm is to use the PID to follow the line.
This will make line following process much smoother, faster and efficient.
12. 6
3.2.2. PID Line Following Algorithm
PID stands for Proportional Integral and Derivative. It is a popular control loop feedback
control extensively used in industrial controls systems. But why would one need a PID
controller for a line following robot, when there are many simpler algorithms already
available for line following.
A conventional robot would follow a line as shown below (red line shows the path taken by
the robot)
Figure 5: Robot track before PID tuning
In the picture the robot oscillates a lot about the line, wasting valuable time and battery
power. Hence there is a maximum speed beyond which you cannot use this algorithm,
otherwise the robot will overshoot the line.
13. 7
A robot with PID control will follow the line as shown below
Figure 6: Robot Track after PID tuning
Target – It is the position you want the line follower to always be (or try to be), that is, the
center of the robot.
Current Position – It is the current position of the robot with respect to the line.
Error - It is the difference between the current position and the target. It can be negative,
positive or zero.
Proportional – It tells us how far the robot is from the line like – to the right, to the extreme
right, to the left or a little to the left. Proportional is the fundamental term used to calculate
the other two.
Integral – It gives the accumulated error over time. It tells us if the robot has been on the line
in the last few moments or not.
Derivative – It is the rate at which the robot oscillates to the left and right about the line.
Kp, Ki and Kd are the constants used to vary the effect of Proportional, Integral and
Derivative terms respectively.
What the controller does is first calculate the current position. Then calculate the error based
on the current position. It will then command the motors to take a hard turn, if the error is
high or a small turn if the error is low. Basically, the magnitude of the turn taken will be
proportional to the error. This is a result of the Proportional control. Even after this, if the
14. 8
error does not decrease or decreases slowly, the controller will then increase the magnitude of
the turn further and further over time till the robot centers over the line. This is a result of the
Integral control. In the process of centering over the line the robot may overshoot the target
position and move to the other side of the line where the above process is followed again.
Thus the robot may keep oscillating about the line in order to center over the line. To reduce
the oscillating effect over time the Derivative control is used. The Pseudo code is given
bellow
//calculate error
Error = target_pos – current_pos
//error times proportional constant gives P
P = Error * Kp
// Integral stores the accumulated error
I = I + Error
//calculates the integral value
I = I * Ki
//stores change in error to derivate
D = Error – Previos_error
Correction = P + I + D
15. 9
4. COMPONENTS
4.1 Electrical Components
1. Arduino mega 2560
Operating voltage: 5 volts
Digital I/O pins: 54
Analog input pins: 16
Clock speed: 16 MHz
EEPROM: 4 KB
SRAM: 8 KB
Flash memory: 256 KB
DC current per I/O pin: 40
mA
Input voltage: 7-12 volt
(Recommended)
2. L298N Motor Drive
Double H bridge drive
chip: L298n
Logical voltage: 5~35 volts
Logical current: 0~36 mA
Maximum power 25 W
Dimensions: 43x43x26
mm
Weight: 26 g
16. 10
3. DC Gear Motor
Rated power: DC 12 volts
Speed: 400 rpm
Shaft diameter: 4 mm
Overall size: 25x67 mm
Weight: 8.7 g
4. Buck Module
Product name: LM2596 step down module
DC-DC buck converter
Power supply: 1.23-30 volts
Input current: 3 A (maximum)
Dimensions: 48x23x14 mm
5. Booster Module
Product name: LM2577 DC-DC
adjustable step up power converter
Output voltage: 4-35 volts
Output current: 2.5 mA (maximum)
Dimensions: 48x23x14 mm
17. 11
6. Battery
Product name: Gens ACE 2200 25C 3S
Li-Po battery
Capacity: 2200 mAh
Discharge rate: 25 C
Dimensions: 106x26x14 mm
Weight: 195 g
Voltage: 11.1 volts
7. IR sensor
Product name: TCRT 5000 Reflective
Optical Sensor
Package type: Leaded
Detector type: Photo transistor
Peak operating system: 2.5 mm
Emitter wavelength: 950 nm
Dimensions: 10.2x5.8x7 mm
8. Arduino Line Following Shield
Power Diode
25 v 100µF capacitor
3 Push switch
1 Buzzer
LED
Serial Rail
18. 12
4.2 Mechanical Components
1. Al-Co Alloy Sheet
2. Caster Ball
Product name: Pololu Ball Caster
Diameter: 1 inch
Height: 1.1-1.4 inch
Ball type: Plastic
3. Wheels
Name : 65 mm wheel
Diameter : 65 mm
Width : 27 mm
Inner six angle
to side : 12mm
Weight : 35g
19. 13
5. CIRCUIT DESIGN
5.1 Motherboard
The motherboard is basically a shield only designed for Arduino MEGA 2560 which can
be easily placed over the arduino. It consists a power diode, one capacitor, three push
switch, one led and serial connections. Its controls all the function of our robot from
controlling the speed of motor to taking raw data from sensor. This shield is the
powerhouse of our robot as it gives power to the whole circuit using a single battery. This
shield made our circuit very concise and saving a lot of space and wires. The shield is
show below in Fig:
Figure 17: Motherboard
Figure 18: Schematics of motherboard
20. 14
5.2 DC Motor Drive
Figure 19: Circuit connection of Motors
For high voltage and high current drive of the DC-motors from ATMEGA16 microcontroller,
L-298 IC has been used which is a dual-bridge controller for motor drive and can be
controlled by sending PWM from the microcontroller into its Enable pin. It supports
bidirectional motor-drive with about 46 volt and 3.5 Ampere. The necessary circuit to drive
DC motor is shown in Fig.8.
5.3 Line Sensor Interfacing
Short range IR transmitter-receiver pair known commercially as TCRT5000 is used as line
sensor. Interfacing pair shown in Fig.12 with the external logic circuitry has been mentioned.
The same circuitry (Fig.11) is used here. In case of white lines, the output is high and for the
black surface (out of the line), the output is low. Using 10 TCRT5000 we made a sensor array
which 20 cm long and distance between each pair of sensor is 2.3 cm apart.
14
21. 15
Figure 20: IR Sensor Array
Figure 21: Principle of Sensor Array
5.4 Overall Electrical System
Figure 22: Overall Electrical Systems
Sensor
Array
Arduino L298N
DC
Motor
22. 16
6. System Prototype
Figure 22: Design of Robot
7. Future Work
In future we will use more robust processing and control using BFS (Breadth First Search),
DFS (Depth First Search) and Dijkustra’s Algorithm for efficient and fast grid solving
8. Conclusion
Our bot is an autonomous mobile robot capable of localizing itself in a 2D world and
planning its path towards destination by means of Flood-Fill Algorithm implemented in
Arduino powered with Atmega2560 microcontroller. It can follow the tracks using feedback
from the line-sensors and also capable of detecting crossings. A large scale prototype
customized for factories will be very promising.
23. 17
9. REFERENCES
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Techniques. A. K.
Peters, Ltd., Wellesley, MA, 1996.
[2] I.J. Cox and G.T. Wilfong, editors. Autonomous Robot Vehicles. Springer,Verlag, 1990.
[3] Joachim Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann,
Frank
Schneider, Jiannis Strikos, and Sebastian Thrun. The mobile robot RHINO. AI Magazine,
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[4] Wolfram Burgard, Dieter Fox, and Sebastian Thrun. Active mobile robot localization. In
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Fifteenth International ConferenceonArtificial Intelligence (IJCAI-97), 1997.
[5] Joachim Hertzberg and Frank Kirchner. Landmark-based autonomous navigation in
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In Proceedings of the First Euromicro Workshop on Advanced Mobile Robots
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[6] Hans P. Moravec. Sensor fusion in certainty grids for mobile robots. AI Magazine, pages
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[7] Illa Nourbakhsh, Rob Powers, and Stan Birch_eld. DERVISH an office-navigating robot.
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[8] Dijkstra, E. W. (1959). "A note on two problems in connexion with graphs". Numerische
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[9] Knuth, Donald E. (1997), The Art Of Computer Programming Vol 1. 3rd ed., Boston:
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Wesley, ISBN 0-201-89683-4, http://www-cs-faculty.stanford.edu/~knuth/taocp.html
[10] Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2001).
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25. 19
10. APPENDIX
10.1 Switch
/////////////////////// Switch Selection ////////////////////////////////////
int getSwitch()
{
if (digitalRead(S1)==HIGH) return 1;
else if (digitalRead(S2)==HIGH) return 2;
else if (digitalRead(S3)==HIGH) return 3;
else return 0;
}
/////////////////////// ThresHold ////////////////////////////////////
void threshHold()
{
while (1) {
if (getSwitch() == 1) {
beepBuzzer();
wheel(150, -150);
for (i = 0; i < 1500; i++)
{
// taking the raw analog values of sensors
for(int j=0;j<10;j++) raw[j] = analogRead(Sensors[j]);
// setting up sensors white values
for(int j=0;j<10;j++) if(raw[j] < white[j]) white[j] =
raw[j];
// setting up sensors black values
for(int j=0;j<10;j++) if(raw[j] > black[j]) black[j] =
raw[j];
}
beepBuzzer();
wheel(-200, 200);
delay(20);
wheel(0, 0);
delay(20);
for (int i = 0; i < 10; i++) {
int w = map(white[i], 0, 1023, 0, 255);
int b = map(black[i], 0, 1023, 0, 255);
//writing values in EEPROM
EEPROM.write(i, w);
EEPROM.write(i + 20, b);
}
break;
}
if (getSwitch() == 2) {
beepBuzzer();
delay(50);
26. 20
beepBuzzer();
for (int i = 0; i < 10; i++) {
white[i] = map(EEPROM.read(i), 0, 255, 0, 1023);
black[i] = map(EEPROM.read(i + 20), 0, 255, 0, 1023);
thresHold[i] = (white[i]+black[i])/2;
}
Serial.println("Grid Info Updated: ");
int k = 100;
for(i=0;i<ROW;i++){
for(j=0;j<COL;j++){
Serial.print(EEPROM.read(k++));
Serial.print(" ");
}
Serial.println();
}
Serial.println("nnPATH: ");
k = 80;
for(i=0;i<index;i++)
{
Serial.print(EEPROM.read(k++));
Serial.print(" >>> ");
}
break;
}
}
}
10.2 Wheel Function
void wheel(int motA,int motB)
{
if(motA==0)
{
digitalWrite(MotorLeftp, HIGH);
digitalWrite(MotorLeftn, HIGH);
}
else if (motA>0)
{
digitalWrite(MotorLeftp, HIGH);
digitalWrite(MotorLeftn, LOW);
}
else if (motA<0)
{
digitalWrite(MotorLeftp, LOW);
digitalWrite(MotorLeftn, HIGH);
}
if(motB==0)
{