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  • Obstacle Avoidance for a Mobile Robot

    GUNNAR GULLSTRAND

    Master’s Degree Project Stockholm, Sweden 2005

    TRITA-NA-E05164

  • Numerisk analys och datalogi Department of Numerical Analysis KTH and Computer Science 100 44 Stockholm Royal Institute of Technology SE-100 44 Stockholm, Sweden

    GUNNAR GULLSTRAND

    TRITA-NA-E05164

    Master’s Thesis in Computer Science (20 credits) at the School of Vehicle Engineering,

    Royal Institute of Technology year 2005 Supervisor at Nada was Patric Jensfelt

    Examiner was Henrik Christensen

    Obstacle Avoidance for a Mobile Robot

  • ABSTRACT Obstacle Avoidance for a Mobile Robot A notorious problem in mobile obstacle avoidance is the detection and avoidance of obstacles. This thesis evaluates several well-known methods for controlling the motion of a mobile robot in an unknown dynamic environment. One of these methods, the Global Dynamic Window Approach, is selected and, using a laser range finder as the only range sensor, the method is implemented and tested on a mobile robot platform, a Pioneer 2 from ActivMedia. The result showed that the method is indeed an effective way for detecting and avoiding obstacles in real-time, in out-door tests the robot has traversed obstacle courses at velocities up to 1.2 metres per second. The method however showed to have some drawbacks; and should be combined with a higher-level algorithm that directs the robot to the best path.

    SAMMANFATTNING Hinderundvikning för en mobil robot Denna rapport beskriver ett examensarbete utfört vid Centrum för Autonoma System, CAS, vid institutionen för numerisk analys och datalogi vid Kungliga Tekniska Högskolan. Målet med examensarbetet var att välja ut och implementera en metod för hinderundvikning för en mobil robot. Rapporten inleds med en bakgrund till robotik och sensorer. Därefter beskrivs ett antal olika metoder för hinderundvikning. En av dessa metoder, the Global Dynamic Window Approach, är utvald och testad på en av de mobila plattformarna vid CAS. Resultaten presenterade här visar att algoritmen är en bra metod till hinderundvikning, i tester gjorda utomhus har plattformen åkt igenom en hinderbana i hastigheter upp till 1.2 meter per sekund. Resultaten i denna rapport bygger på att roboten använder en laser skanner som sin enda avståndsmätare, resultaten är inte nödvändigtvis samma som om andra sensorer hade använts. Tyvärr är inte metoden helt fulländad, i slutet av rapporten diskuteras hur den skulle kunna kombineras med ett beteende på hög nivå som ger metoden anvisningar om t.ex. vägval.

  • ACKNOWLEDGEMENTS A completed task is always preceded by a chain of events. All of which we are not in control or even aware of. Many actions have guided me towards completing this thesis. Some dating back to when my parents persuaded me to study when I was young and some to when my thesis supervisor did the same a few years later. I cannot thank my family enough for the support and understanding. And I cannot thank my supervisor Patric Jensfelt enough for the hours of support and time to answer stupid questions. Without you the code would be nothing but a big segmentation fault and the thesis would have to wait a few more years. I would also like to express my sincere gratitude to Prof. Henrik I. Christensen and all the people at the Centre for Autonomous Systems. My time at CAS has been very pleasant and educational.

  • PREFACE This M.Sc. Project has been carried out at the Centre for Autonomous Systems (CAS), which is a research centre at the Royal Institute of Technology in Stockholm. The centre does research in autonomous systems including mobile robot systems for manufacturing and domestic applications [23]. CAS has several robotic systems and some of them are presented in short in the appendix.

    Assignment In mobile robotics one of the major areas of concern is how to control the robot’s movements, making the robot move without hitting any obstacles. The algorithm making these decisions is called an obstacle avoidance algorithm. The motivation for this thesis is to find a way to improve the obstacle avoidance algorithm that is in use at CAS. The current algorithm is built on a technique called the Vector Field Histogram, a method that has several inherent drawbacks, probably most serious of these is that it in no way considers the dynamics of the robot. A new way of controlling the robots was requested and I was given free reins, I was to do a literature study on the obstacle avoidance and implement the method that I found would yield the best result. The title, “Obstacle Avoidance for a Mobile Robot” well defines what this thesis is about, it is fairly easy to decide what is part of the thesis and what is not. Obstacle avoidance is however an important part of a robotic system, it is a big research field that had its peak a few years ago and many theories and methods have been put forth. It is not possible to present but a small subset of the different ideas here. The selected methods are however some of the more important ideas and studying them give a good understanding of obstacle avoidance. The requirements that were put on the algorithm were that it should be fast, robust and not dependent on prior information about the environment. It has to be fast for several reasons, firstly because a fast algorithm lets the robot react to changes in the environment quickly. It also means that the robot will be able to drive at higher velocities. Low computation time also lets several processes run concurrently on the CPU so that the robot can do more things than just avoid obstacles at the same time. Naturally the algorithm and especially the implementation also have to be robust. It is trusted to manoeuvre an expensive robot in environments where people and other robots are present so it must not fail, allowing the robot to collide. Nor can the algorithm depend on prior information since the surroundings can change quickly, furniture can be moved and people walk about. This information is not present in any map so the robot has to decide as it goes where it can drive.

  • TABLE OF CONTENTS 1. Introduction ..................................................................................................... 1

    1.1. Robot behaviour....................................................................................... 1 1.2. Robot Architectures.................................................................................. 2 1.3. Avoiding obstacles ................................................................................... 3

    2. Sensors and Actuators...................................................................................... 4

    2.1. Sensors .................................................................................................... 4 2.2. Actuators ................................................................................................. 6

    3. Representing the Environment.......................................................................... 8

    3.1. Occupancy grid ........................................................................................ 9 4. Obstacle Avoidance ........................................................................................10

    4.1. Potential fields ........................................................................................10 4.2. The Vector Field Histogram Method........................................................12 4.3. The Dynamic Window Approach.............................................................14 4.4. The Global Dynamic Window Approach .................................................17 4.5. Conclusion ..............................................................................................19

    5. Algorithm.......................................................................................................20

    5.1. The Map .................................................................................................20 5.2. The NF1..................................................................................................21 5.3. The GDWA.............................................................................................22

    6. Experiments and Results .................................................................................24

    6.1. The Map Size..........................................................................................24 6.2. The NF1..................................................................................................27 6.3. The GDWA.............................................................................................27 6.4. Outdoors .................................................................................................31 6.5. Conclusions ............................................................................................32

    7. Summary and Conclusions..............................................................................33 8. Future Work ...................................................................................................34 References..........................................................................................................35

  • 1

    1. INTRODUCTION Mobile robot systems currently available on the market vary from autonomous dust- busters and lawnmowers to pet toys. Prices can in many ca

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