Knowledge-Based Vision-Guided Robots

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. Front Matter....Pages I-XII Introduction....Pages 1-8 Related Systems and Ideas....Pages 9-43 Embodied Vision For Mobile Robots....Pages 45-62 Object Recognition Mobile Robot Guidance....Pages 63-86 Edge Segmentation and Matching....Pages 87-107 Knowledge Based Shape from Shading....Pages 109-134 Supporting Navigation Components....Pages 135-145 Fuzzy Control for Active Perceptual Docking....Pages 147-164 System Results and Case Studies....Pages 165-202 Conclusion....Pages 203-207 Back Matter....Pages 209-234 Introduction Related Systems and Ideas Embodied Vision for Mobile Robots Object Recognition for Visual Guidance of a Mobile Robot Edge Segmentation and Matching Knowledge Based Shape from Shading Supporting Navigation Components Fuzzy Control for Active Perceptual Docking System Results and Case Studies Conclusion. The complete table of contents can be found on the Internet: http://www.springer.de.