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Path Traversal Around Obstacles by a Robot Using Terrain Marks For Path Traversal Around Obstacles by a Robot Using Terrain Marks for Guidance A thesis submitted to the Graduate School of University of Cincinnati In partial fulfillment of the requirements for the degree of Master of Science In the School of Electronic and Computing Systems of the College of Engineering and Applied Science April 2011 by Rabindra Pannu B.Tech. in Computer Engineering Kurukshetra University, Kurukshetra, India Thesis Advisor and Committee Chair: Dr.Raj Bhatnagar Abstract The problem of autonomous robots avoiding obstacles while traversing a terrain requires efficient algorithms. There has been much research work done for the cases where the locations of obstacles are known before the path is planned. We present here an algorithm for the case in which the robot has no prior knowledge about the locations of the obstacles. The robot starts its navigation knowing the coordinates of the start and the destination points and adjusts its path as it encounters obstacles. Many existing algorithms are designed for the contexts in which the robot determines its location based on information provided by either the GPS system or an overhead camera. Our algorithm has been developed for the context in which the robot determines its (x, y) coordinates based on the grid marks on the terrain, and does not depend on the global systems for positioning cues. We have shown that our algorithm can successfully traverse the terrain for most of the situations even though there are some exception cases in which the robot gets stuck while there still is a possible path through the maze of obstacles. The robot also returns to the starting location, navigating only the shortest path running through the cells navigated while going towards the destination. This thesis describes the algorithm, implementation details for the Khepera robot used, and the results obtained with our algorithm. Acknowledgements I would like to take this opportunity to express my gratitude to my thesis advisor, Dr. Raj Bhatnagar, whose guidance, encouragement and patience, inspired me to work on this project. I am extremely grateful to him for his support and co-operation which helped me in successfully completing my Master's degree. I would also like to thank Dr. Carla Purdy, Dr. Karen Davis and Dr. Manish Kumar for their presence on my thesis committee. I would also like to thank Robert Montjoy for his technical support in the research lab. Most importantly, I would like to thank my family and friends for their love and support. I would like to dedicate this work to them. Contents 1 Introduction1 1.1 Introduction..................................1 1.2 Motivation...................................2 1.3 Our Approach.................................3 1.4 Organization of the Thesis..........................5 2 Literature Review6 2.1 Introduction..................................6 2.2 Offline and Online Algorithms........................7 2.2.1 Offline Algorithms..........................7 2.2.2 Online Algorithms..........................8 2.3 Maze Traversal................................8 2.4 Path Planning and Motion Control..................... 11 2.5 Problem Formulation............................. 14 2.6 Technical Specifications............................ 15 2.6.1 Hardware specifications........................ 16 2.6.2 Software Specifications........................ 17 2.7 Constraints of the Environment....................... 18 i 3 Strategy and Algorithms 20 3.1 Introduction.................................. 20 3.2 Terms Used.................................. 21 3.3 Path Planning and Obstacle Avoidance................... 23 3.3.1 Navigation............................... 23 3.3.2 Obstacle Database Creation..................... 24 3.3.3 Intermediate Goal Formation.................... 26 3.3.4 Blacklisting and Backtracking.................... 29 3.3.5 Strategy Decision........................... 30 3.4 Algorithms................................... 32 3.4.1 Main Algorithm............................ 32 3.4.2 Path Planning Procedure....................... 35 3.4.3 Path Planning in the X Direction.................. 35 3.4.4 Path Planning in the Y Direction.................. 36 3.4.5 Obstacle Avoidance Procedure.................... 37 3.4.6 Intermediate Goal Formation.................... 38 3.4.7 Backtrack............................... 40 3.5 Flowchart for the Maze Traversal Algorithm................ 40 4 Results and Analysis 42 4.1 Introduction.................................. 42 4.2 Test Cases................................... 43 4.2.1 Test Case 1.............................. 43 4.2.2 Test Case 1 Analysis......................... 46 4.2.3 Test Case 2.............................. 47 ii 4.2.4 Test Case 2 Analysis......................... 50 4.2.5 Test Case 3.............................. 51 4.2.6 Test Case 3 Analysis......................... 53 4.2.7 Test Case 4.............................. 54 4.2.8 Test Case 4 Analysis......................... 57 4.3 Test Cases Strategy Dependent Analysis.................. 58 4.3.1 Test Case 1 Left Strategy...................... 58 4.3.2 Test Case 2 Left Strategy...................... 60 4.3.3 Test Case 3 Left Strategy...................... 62 4.3.4 Test Case 4 Left Strategy...................... 63 5 Conclusions and Future Work 65 5.1 Conclusion................................... 65 5.2 Future Work.................................. 67 iii List of Algorithms 1 Path Planning and Obstacle Avoidance Algorithm ......... 33 2 Path Planning ................................ 35 3 Path For X .................................. 36 4 Path For Y .................................. 37 5 Obstacle Avoidance ............................. 38 6 Intermediate Goal .............................. 39 7 Backtrack ................................... 40 iv List of Figures 1.1 Sample maze design..............................4 2.1 Grid used in the thesis............................ 15 2.2 Khepera Robot................................ 16 2.3 Different Views of the Khepera Robot.................... 18 3.1 Intermediate Goal............................... 27 3.2 Backtracking................................. 29 3.3 Flowchart for the Maze Traversal Algorithm................ 41 4.1 Test Case 1.................................. 44 4.2 Test Case 2.................................. 49 4.3 Test Case 3.................................. 52 4.4 Test Case 4.................................. 55 4.5 Test case 1 Strategy Comparison...................... 58 4.6 Test case 2 Strategy Comparison...................... 60 4.7 Test Case 2 Alternate Goal Strategy Analysis............... 61 4.8 Test Case 3.................................. 62 4.9 Test Case 4 Left Strategy.......................... 63 v List of Tables 4.1 Test Case 1 Path details........................... 45 4.2 Test Case 1 Analysis............................. 47 4.3 Test Case 2 Path details........................... 48 4.4 Test Case 2 Analysis............................. 50 4.5 Test Case 3 Path details........................... 53 4.6 Test Case 3 Analysis............................. 53 4.7 Test Case 4 Path details........................... 56 4.8 Test Case 4 Analysis............................. 57 4.9 Test Case 1 Left Strategy Analysis..................... 59 4.10 Test Case 1 Strategy Wise Analysis..................... 59 4.11 Test Case 2 Left Strategy Analysis..................... 61 4.12 Test Case 2 Strategy Wise Analysis..................... 61 4.13 Test Case 3 Strategy Wise Analysis..................... 63 4.14 Test Case 4 Left Strategy Analysis..................... 64 4.15 Test Case 4 Strategy Wise Analysis..................... 64 vi Chapter 1 Introduction 1.1 Introduction Navigating agents or mobile autonomous robots in a problem space is a significant area of interest for many researchers in the field of Artificial Intelligence. These applications require robots to navigate without colliding into the obstacles present in a work space environment and reach their destinations safely. Modeling the real-world environment is highly complex and in certain situations not a feasible task. The robot in such situations is expected to operate in the most efficient manner even though provided with limited information regarding the working environment [12]. Navigating through a cluttered workspace to reach a destination point or to move material on a factory floor can be viewed as maze traversal problems and the algorithms developed for maze traversal can be applied for these applications as well. Maze traversal, maze escape, finding shortest and most cost effective path between a start and a destination point etc. have been some highly intriguing and difficult problems that scholars have been working on for decades. Real time motion control, unpredictable and dynamic environment, obstacle avoidance, 1 navigation, communication are some of the issues that the algorithm designers have to face when dealing with such systems. This thesis proposes a grid based maze traversal algorithm for a mobile robot to reach its goal coordinates from the start coordinates in an unknown environment and return to the start coordinates using the most efficient navigated path. Movement of materials on a factory floor, search and rescue operations in a natural disaster or war situations, performing tasks in situations which are dangerous for humans such as hazardous material disposal, environmental protection, assisting in dangerous war missions including surveillance, bomb detection and detonation, toxic waste disposal, extra-terrestrial and underwater
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