Knowledge-Based Vision-Guided Robots
Published on 6. August 2002
Book
Hardback
X, 212 pages
978-3-7908-1494-1 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Heidelberg
Germany
Target group
College/higher education
Professional and scholarly
Illustrations
3
174 s/w Abbildungen, 3 s/w Tabellen
99 figs., 3 tabs.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
485 gr
ISBN-13
978-3-7908-1494-1 (9783790814941)
DOI
10.1007/978-3-7908-1780-5
Schweitzer Classification
Other editions
Additional editions
Nick Barnes | Zhi-Quiang Liu
Knowledge-Based Vision-Guided Robots
Book
08/2012
Physica
€53.49
Shipment within 7-9 days
Content
1 Introduction.- 1.1 Background.- 1.1.1 A vision-guided approach.- 1.1.2 Computer vision and vision-guided mobile robots.- 1.1.3 Applying high-level computer vision to guide mobile robots.- 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.- 2.1.1 Frame-based computer vision.- 2.1.2 Active vision.- 2.2 Vision-Guided Mobile Robot Systems.- 2.2.1 Mobile robot subsystems and concepts.- 2.2.2 Mobile robot object recognition.- 2.2.3 Maps and path planning.- 2.2.4 Temporal sequencing for complex tasks.- 2.2.5 Vision-guided mobile robot systems.- 2.2.6 Reactive navigation.- 2.2.7 Model-based vision systems for mobile robots.- 2.2.8 Knowledge-based mobile robotic systems.- 2.2.9 Vision-guided mobile robots using stereo.- 2.2.10 Active perception systems for mobile robots.- 2.2.11 Application of vision-guided mobile robots.- 2.3 Computer Vision for Mobile Robots.- 2.3.1 Traditional model-based vision 3D object recognition.- 2.3.2 Shape-from-shading.- 2.3.3 Pose determination.- 2.4 Conclusion.- 3 Embodied Vision For Mobile Robots.- 3.1 Introduction.- 3.1.1 Embodiment.- 3.1.2 Phenomena and noumena.- 3.2 The Classical Computer Vision Paradigm.- 3.2.1 Non-classical computer vision.- 3.3 Problems with Classical Computer Vision.- 3.4 Applying Embodied Concepts in Human Vision.- 3.4.1 Models play an analogous role in computer vision.- 3.5 Embodiment of Vision-guided Robots.- 3.5.1 Embodiment, task and environment.- 3.5.2 The role of the task.- 3.5.3 The role of the environment.- 3.6 Embodiment for Vision-guided Robots.- 3.6.1 Physical embodiment.- 3.6.2 Embodiment in a task.- 3.6.3 Embodiment in an environment.- 3.7 Conclusion.- 4 Object Recognition Mobile Robot Guidance.- 4.1 Introduction.- 4.2 System Perspective.- 4.3 Object Recognition.- 4.3.1 Canonical-views.- 4.3.2 Match verification.- 4.3.3 Edge matching.- 4.3.4 Edge-based features for ground-based robots.- 4.3.5 View prediction.- 4.4 Determining Object Pose and Distance.- 4.4.1 Active determination of the sign of ?.- 4.4.2 Error analysis.- 4.5 Conclusion.- 5 Edge Segmentation and Matching.- 5.1 Edge Extraction.- 5.1.1 Edge extraction.- 5.1.2 On the choice of window size and quantisation of ? and ?.- 5.2 Edge Matching.- 5.2.1 Evaluating matches.- 5.2.2 Spatial elimination.- 5.2.3 Edge coverage.- 5.2.4 Position estimation consistency.- 5.2.5 Geometric verification.- 5.2.6 Quadratic edge extraction.- 5.2.7 Further active processing.- 6 Knowledge Based Shape from Shading.- 6.1 Introduction.- 6.1.1 Motivation and system perspective.- 6.1.2 Assumptions.- 6.1.3 Knowledge-based representation of objects.- 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.- 6.4.1 Knowledge / frame topology.- 6.4.2 Fact knowledge.- 6.4.3 Procedural knowledge.- 6.4.4 Shape processing rulebase.- 6.5 Experimental Method and Results.- 6.5.1 Synthetic images.- 6.5.2 Real images.- 6.5.3 Domain knowledge.- 6.6 Conclusion.- 7 Supporting Navigation Components.- 7.1 Model-based Path Planning.- 7.1.1 Path planning and obstacle avoidance.- 7.2 Odometry and Obstacle Avoidance Subsystem.- 7.2.1 Obstacle avoidance strategies.- 7.2.2 Coordinate transforms.- 8 Fuzzy Control for Active Perceptual Docking.- 8.1 Introduction.- 8.1.1 Fuzzy control.- 8.1.2 Fuzzy control for mobile robot control.- 8.1.3 TSK fuzzy model.- 8.1.4 Visual motion-based approaches to mobile robots and the docking problem.- 8.2 Direction Control for Robot Docking.- 8.2.1 The log-polar camera.- 8.2.2 Docking for a ground-based robot.- 8.2.3 Noise in the input parameter.- 8.3 A Fuzzy Control Scheme.- 8.4 Results.- 8.5 Conclusion.- 9 System Results and Case Studies.- 9.1 Evaluation of Components.- 9.1.1 Experimental setup.- 9.1.2 View matching.- 9.1.3 Pose determination - power supply.- 9.1.4 Pose determination - model vehicle.- 9.2 Case Studies.- 9.2.1 Moving around the corner of an object.- 9.2.2 Distinguishing a particular object among similar objects.- 9.2.3 Docking.- 9.2.4 Object circumnavigation.- 9.2.5 Obstacle avoidance.- 9.3 Conclusion.- 10 Conclusion.- 10.1 Limitations of the Research Presented and Future Work.- 10.2 Extended quotation from Descartes.