Knowledge-Based Vision-Guided Robots

by Nick Barnes, Zhi-Quiang Liu

Overview

Many robotics researchers consider high-level vision algorithms (computational) too expensive for use in robot guidance. This book introduces the reader to an alternative approach to perception for autonomous, mobile robots. It explores how to apply methods of high-level computer vision and fuzzy logic to the guidance and control of the mobile robot. The book introduces a knowledge-based approach to vision modeling for robot guidance, where advantage is taken of constraints of the robot's physical structure, the tasks it performs, and the environments it works in. This facilitates high-level computer vision algorithms such as object recognition at a speed that is sufficient for real-time navigation. The texts presents algorithms that exploit these constraints at all levels of vision, from image processing to model construction and matching, as well as shape recovery. These algorithms are demonstrated in the navigation of a wheeled mobile robot.

Product Details

ISBN-13:	9783662003121
Publisher:	Physica-Verlag HD
Publication date:	08/02/2012
Series:	Studies in Fuzziness and Soft Computing , #103
Edition description:	Softcover reprint of the original 1st ed. 2002
Pages:	212
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1 Introduction.- 1.1 Background.- 1.2 Aims of the Research Presented in this Book: A Problem in Robot Vision.- 1.3 The Approach of this Book.- 1.4 About the Chapters.- 2 Related Systems and Ideas.- 2.1 Basic computer vision approaches.- 2.2 Vision-Guided Mobile Robot Systems.- 2.3 Computer Vision for Mobile Robots.- 2.4 Conclusion.- 3 Embodied Vision For Mobile Robots.- 3.1 Introduction.- 3.2 The Classical Computer Vision Paradigm.- 3.3 Problems with Classical Computer Vision.- 3.4 Applying Embodied Concepts in Human Vision.- 3.5 Embodiment of Vision-guided Robots.- 3.6 Embodiment for Vision-guided Robots.- 3.7 Conclusion.- 4 Object Recognition Mobile Robot Guidance.- 4.1 Introduction.- 4.2 System Perspective.- 4.3 Object Recognition.- 4.4 Determining Object Pose and Distance.- 4.5 Conclusion.- 5 Edge Segmentation and Matching.- 5.1 Edge Extraction.- 5.2 Edge Matching.- 6 Knowledge Based Shape from Shading.- 6.1 Introduction.- 6.2 Using Object Model Knowledge for Shape-From-Shading.- 6.3 A New Boundary Condition for Shape-From-Shading.- 6.4 Knowledge-based Implementation.- 6.5 Experimental Method and Results.- 6.6 Conclusion.- 7 Supporting Navigation Components.- 7.1 Model-based Path Planning.- 7.2 Odometry and Obstacle Avoidance Subsystem.- 8 Fuzzy Control for Active Perceptual Docking.- 8.1 Introduction.- 8.2 Direction Control for Robot Docking.- 8.3 A Fuzzy Control Scheme.- 8.4 Results.- 8.5 Conclusion.- 9 System Results and Case Studies.- 9.1 Evaluation of Components.- 9.2 Case Studies.- 9.3 Conclusion.- 10 Conclusion.- 10.1 Limitations of the Research Presented and Future Work.- 10.2 Extended quotation from Descartes.