One of the series in Machine Perception and Artificial Intelligence, this book covers subjects including the Harvard binocular head; heads, eyes, and head-eye systems; a binocular robot head with torsional eye movements; and escape and dodging behaviours for reactive control.

Contents:Editorial (H I Christensen et al.)The Harvard Binocular Head (N J Ferrier & J J Clark)Heads, Eyes, and Head-Eye Systems (K Pahlavan & J-O Eklundh)Design and Performance of TRISH, a Binocular Robot Head with Torsional Eye Movements (E Milios et al.)A Low-Cost Robot Camera Head (H I Christensen)The Surrey Attentive Robot Vision System (J R G Pretlove & G A Parker)Layered Control of a Binocular Camera Head (J L Crowley et al.)SAVIC: A Simulation, Visualization and Interactive Control Environment for Mobile Robots (C Chen & M M Trivedi)Simulation and Expectation in Sensor-Based Systems (Y Roth & R Jain)Active Avoidance: Escape and Dodging Behaviors for Reactive Control (R C Arkin et al.) Readership: Engineers and computer scientists. keywords:Active Vision;Robot Vision;Computer Vision;Model-Based Vision;Robot Navigation;Reactive Control;Robot Motion Planning;Knowledge-Based Vision;Robotics

Foreword by Michael Arbib This introduction to the principles, design, and practice of intelligent behavior-based autonomous robotic systems is the first true survey of this robotics field. The author presents the tools and techniques central to the development of this class of systems in a clear and thorough manner. Following a discussion of the relevant biological and psychological models of behavior, he covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence. The text throughout refers to actual implemented robots and includes many pictures and descriptions of hardware, making it clear that these are not abstract simulations, but real machines capable of perception, cognition, and action.

Now in its third edition, this textbook is a comprehensive introduction to the multidisciplinary field of mobile robotics, which lies at the intersection of artificial intelligence, computational vision, and traditional robotics. Written for advanced undergraduates and graduate students in computer science and engineering, the book covers algorithms for a range of strategies for locomotion, sensing, and reasoning. The new edition includes recent advances in robotics and intelligent machines, including coverage of human-robot interaction, robot ethics, and the application of advanced AI techniques to end-to-end robot control and specific computational tasks. This book also provides support for a number of algorithms using ROS 2, and includes a review of critical mathematical material and an extensive list of sample problems. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.

This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book provides an excellent sample of current concepts, examples and applications from multiple areas of computer vision.

An authoritative and accessible one-stop resource, An Introduction to Artificial Intelligence presents the first full examination of AI. Designed to provide an understanding of the foundations of artificial intelligence, it examines the central computational techniques employed by AI, including knowledge representation, search, reasoning, and learning, as well as the principal application domains of expert systems, natural language, vision, robotics, software agents and cognitive modeling. Many of the major philosophical and ethical issues of AI are also introduced. Throughout the volume, the authors provide detailed, well-illustrated treatments of each topic with abundant examples and exercises. The authors bring this exciting field to life by presenting a substantial and robust introduction to artificial intelligence in a clear and concise coursebook form. This book stands as a core text for all computer scientists approaching AI for the first time.

This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs). The PIN, which forms the heart of the system and which is based on Bayesian probabilistic networks, exhibits potential for application in several areas of computer vision as well as a range of other spatial reasoning tasks. The text includes a highly comprehensive, classificatory review of prior work in perceptual organization and, within that framework, identifies key areas for future work by the computer vision research community.