基于视觉的自主机器人导航 [Vision Based Autonomous Robot Navigation].pdf

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Title

Preface

Contents

Mobile Robot Navigation

Autonomous Mobile Robot Navigation

Why Vision in Navigation?

Vision-Based Navigation

Vision Based Indoor Navigation

Vision Based Outdoor Navigation

State of the Art

Obstacle Detection and Avoidance

Summary

References

Interfacing External Peripherals with a Mobile Robot

Introduction

PIC Microcontroller Based System for Interfacing a Vision System with a Ready-Made Robot

The Integrated System Employing KOALA Robot with a PC and a Vision System

Real-Life Performance Evaluation

Summary

References

Vision-Based Mobile Robot Navigation Using Subgoals

Introduction

The Hardware Setup

A Two-Layer, Goal Oriented Navigation Scheme

Image Processing Based Exploration of the Environment in Layer 1

Shortest Path Computation and Subgoal Generation

Indigenous Development of Vision-Based Mobile Robots

Introduction

Development of a Low-Cost Vision Based Mobile Robot

Development of Microcontroller Based Sensor Systems for Such Robots

IR Range Finder System with Dynamic Enhancement1

Optical Proximity Detectors Using Switching-Mode Synchronous Detection Technique2

The Intranet-Connectivity for Client-Server Operation

Summary

References

Sample Implementations of Vision-Based Mobile Robot Algorithms

Introduction

Lesson 1

Lesson 2

Lesson 3

Lesson 4

Lesson 5

Lesson 6

Lesson 7

Lesson 8

Lesson 9

Lesson 10

Summary

References

Vision Based Mobile Robot Path/Line Tracking

Introduction

A Preview of the Proposed Scheme

A Fuzzy System for Vision Based Robot Navigation

The IR-Sensor Based Obstacle Avoidance by Employing a Fuzzy Algorithm

Real-Life Performance Evaluation

Summary

References

Simultaneous Localization and Mapping (SLAM) in Mobile Robots

Introduction

Extended Kalman Filter (EKF) Based Stochastic SLAM Algorithm

Neuro-fuzzy Assistance for EKF Based SLAM

The Neuro-fuzzy Architecture and Its Training Methodology Employing Particle Swarm Optimization (PSO)

Architecture of the Neuro-fuzzy Model

Training the Neuro-fuzzy Model Employing PSO

Performance Evaluation

Training a Fuzzy Supervisor Employing Differential Evolution (DE) Based Optimization

Performance Evaluation

Summary

References

Vision Based SLAM in Mobile Robots

Introduction

The Dynamic State Model for the Differential Drive Koala Robot

Vision Sensing Based Image Feature Identification, Feature Tracking and 3d Distance Calculation for Each Feature

Real-Life Performance Evaluation

Summary

References

Index

Studies in Computational Intelligence 455 Editor-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl For further volumes: http://www.springer.com/series/7092

Amitava Chatterjee, Anjan Rakshit, and N. Nirmal Singh Vision Based Autonomous Robot Navigation Algorithms and Implementations ABC

Authors Dr. Amitava Chatterjee Electrical Engineering Department Jadavpur University West Bengal Kolkata India Prof. Dr. Anjan Rakshit Electrical Engineering Department Jadavpur University West Bengal Kolkata India Dr. N. Nirmal Singh Electronics and Communication Engineering Department V V College of Engineering Tuticorin District TamilNadu Tisaiyanvilai India Additional material to this book can be downloaded from http://extras.springer.com ISSN 1860-949X ISBN 978-3-642-33964-6 DOI 10.1007/978-3-642-33965-3 Springer Heidelberg New York Dordrecht London e-ISSN 1860-9503 e-ISBN 978-3-642-33965-3 Library of Congress Control Number: 2012949068 c Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied speciﬁcally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of pub- lication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface “Vision Based Autonomous Robot Navigation: Algorithms and Implementations” is devoted to the theory and development of autonomous navigation of mobile robots using computer vision based sensing mechanism. The conventional robot navigation systems, utilizing traditional sensors like ultrasonic, IR, GPS, laser sensors etc., suffer several drawbacks related to either the physical limitations of the sensor or incur high cost. Vision sensing has emerged as a popular alternative where cameras can be used to reduce the overall cost, maintaining high degree of intelligence, flexibility and robustness. The introductory chapter details the basic concepts of autonomous navigation of mobile robots and the utility of using vision as the sensing mechanism in this context is highlighted. Here a broad categorization of research activities pertaining to vision-based navigation in indoor and outdoor environments is presented. This is followed by an introduction of different broad modalities of obstacle detection and avoidance. In the next chapter, the book discusses how real-life interfacing of external peripherals with a readymade mobile robot can be successfully achieved. Here a detail description of interfacing of such peripherals with the KOALA robot using serial communication in interrupt driven mode is provided. In the next chapter, a vision based robot navigation strategy is detailed, where a subgoal based scheme is employed to follow the shortest path to reach the final goal and also simultaneously achieve the desired obstacle avoidance. This strategy employs a two layer architecture where vision sensor operates in layer 1 and IR sensor based obstacle avoidance scheme operates in layer 2. The next chapter discusses how a low-cost robot can be indigenously developed in the laboratory with special functionalities. Special emphasis is put on development of two microcontroller based sensor systems for the robot in this regard: (i) an IR range finder system that can be developed with dynamic range enhancement capability and (ii) an optical proximity detector system developed utilizing the principle of switching mode synchronous detection technique. This is followed by the next chapter which presents, in a step-by-step manner, gradually progressing from easier modules to more complex modules, how vision-based navigation subroutines can be actually implemented in real-life, under 32-bit Windows environment. The next two chapters deal with incorporation of fuzzy logic in the context of mobile robot navigation. Among these, the first one discusses how a vision based navigation scheme can be developed for indoor path/line tracking. Here fuzzy vision-based navigation is hybridized with a fuzzy IR-based obstacle avoidance mechanism. The next chapter first introduces the concept of EKF-based SLAM for mobile robots. Then it discusses a more complex scenario where fuzzy

VI Preface or neuro-fuzzy supervision can be effectively utilized to improve performance for EKF based SLAM in presence of incorrect or uncertain knowledge of sensor statistics. The last chapter discusses how a two camera based vision system can be implemented in reality for SLAM in an indoor environment. Kolkata, West Bengal, India September 2012 Amitava Chatterjee Anjan Rakshit N. Nirmal Singh

Contents 1 Mobile Robot Navigation ................................................................................. 1 1.1 Autonomous Mobile Robot Navigation ..................................................... 1 1.2 Why Vision in Navigation? ........................................................................ 1 1.3 Vision-Based Navigation ........................................................................... 3 1.3.1 Vision Based Indoor Navigation ...................................................... 4 1.3.1.1 Map-Based Navigation ..................................................... 4 1.3.1.2 Map-Building-Based Navigation ...................................... 4 1.3.1.3 Mapless Navigation .......................................................... 5 1.3.2 Vision Based Outdoor Navigation .................................................... 6 1.4 State of the Art ........................................................................................... 6 1.5 Obstacle Detection and Avoidance ...........................................................12 1.6 Summary ...................................................................................................14 References .........................................................................................................14 2 Interfacing External Peripherals with a Mobile Robot ............................... 21 2.1 Introduction ...............................................................................................21 2.2 PIC Microcontroller Based System for Interfacing a Vision System with a Ready-Made Robot ........................................................................23 2.3 The Integrated System Employing KOALA Robot with a PC and a Vision System .................................................................................34 2.4 Real-Life Performance Evaluation ............................................................39 2.5 Summary ...................................................................................................45 Acknowledgement............................................................................................. 45 References .........................................................................................................45 3 Vision-Based Mobile Robot Navigation Using Subgoals ............................. 47 3.1 Introduction ...............................................................................................47 3.2 The Hardware Setup ..................................................................................49 3.3 A Two-Layer, Goal Oriented Navigation Scheme ....................................52 3.4 Image Processing Based Exploration of the Environment in Layer 1 .......53 3.5 Shortest Path Computation and Subgoal Generation ................................58 3.6 IR Based Navigation in Layer 2 ................................................................62 3.7 Real-Life Performance Evaluation ............................................................63 3.8 Summary ...................................................................................................80 Acknowledgement............................................................................................. 81 References .........................................................................................................81

VIII Contents 4 Indigenous Development of Vision-Based Mobile Robots ........................... 83 4.1 Introduction ...............................................................................................83 4.2 Development of a Low-Cost Vision Based Mobile Robot ........................84 4.3 Development of Microcontroller Based Sensor Systems for Such Robots .........................................................................................85 4.3.1 IR Range Finder System with Dynamic Range Enhancement ........ 85 4.3.1.1 The Dynamic Range Enhancement Algorithm..................88 4.3.1.2 Experimental Results ........................................................89 4.3.2 Optical Proximity Detectors Using Switching-Mode Synchronous Detection Technique ....................................................................... 89 4.3.2.1 PIC Microcontroller Based Optical Proximity Detector ............................................................................90 4.3.2.2 Switching Mode Synchronous Detection (SMSD) Technique ..........................................................................94 4.3.2.3 Experimental Results ........................................................96 4.4 The Intranet-Connectivity for Client-Server Operation ............................97 4.5 Summary ...................................................................................................99 References .......................................................................................................100 5 Sample Implementations of Vision-Based Mobile Robot Algorithms ...... 101 5.1 Introduction ............................................................................................ 101 5.2 Lesson 1 .................................................................................................. 102 5.3 Lesson 2 .................................................................................................. 108 5.4 Lesson 3 .................................................................................................. 113 5.5 Lesson 4 .................................................................................................. 116 5.6 Lesson 5 .................................................................................................. 119 5.7 Lesson 6 .................................................................................................. 124 5.8 Lesson 7 .................................................................................................. 129 5.9 Lesson 8 .................................................................................................. 132 5.10 Lesson 9 ................................................................................................ 134 5.11 Lesson 10 .............................................................................................. 137 5.12 Summary ............................................................................................... 141 References ...................................................................................................... 142 6 Vision Based Mobile Robot Path/Line Tracking ....................................... 143 6.1 Introduction ............................................................................................ 143 6.2 A Preview of the Proposed Scheme ........................................................ 144 6.3 A Fuzzy System for Vision Based Robot Navigation ............................. 146 6.4 The IR-Sensor Based Obstacle Avoidance by Employing a Fuzzy Algorithm ............................................................................................... 155 6.5 Real-Life Performance Evaluation ......................................................... 158 6.6 Summary ................................................................................................. 165 Acknowledgement .......................................................................................... 165 References ...................................................................................................... 165

Contents IX 7 Simultaneous Localization and Mapping (SLAM) in Mobile Robots ...... 167 7.1 Introduction ............................................................................................ 167 7.2 Extended Kalman Filter (EKF) Based Stochastic SLAM Algorithm ..... 170 7.3 Neuro-fuzzy Assistance for EKF Based SLAM ..................................... 176 7.4 The Neuro-fuzzy Architecture and Its Training Methodology Employing Particle Swarm Optimization (PSO) .................................... 180 7.4.1 Architecture of the Neuro-fuzzy Model ...................................... 180 7.4.2 Training the Neuro-fuzzy Model Employing PSO ...................... 181 7.4.3 Performance Evaluation .............................................................. 184 7.5 Training a Fuzzy Supervisor Employing Differential Evolution (DE) Based Optimization ....................................................................... 193 7.5.1 Performance Evaluation .............................................................. 194 7.6 Summary ................................................................................................. 203 Acknowledgement .......................................................................................... 203 References ...................................................................................................... 203 8 Vision Based SLAM in Mobile Robots ........................................................ 207 8.1 Introduction ............................................................................................ 207 8.2 The Dynamic State Model for the Differential Drive Koala Robot ........ 208 8.3 Vision Sensing Based Image Feature Identification, Feature Tracking and 3D Distance Calculation for Each Feature ....................................... 211 8.4 Real-Life Performance Evaluation ......................................................... 215 8.5 Summary ................................................................................................. 220 Acknowledgement .......................................................................................... 221 References ...................................................................................................... 221 Index ................................................................................................................... 223

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