The goal of autonomous mobile robotics is to build and control physical systems which can move purposefully and without human intervention in real-world environments which have not been specifically engineered for the robot. The development of techniques for autonomous mobile robot operation constitutes one of the major trends in the current research and practice in modern robotics. This volume presents a variety of fuzzy logic techniques which address the challenges posed by autonomous robot navigation. The focus is on four major problems: how to design robust behavior-producing control modules; how to use data from sensors for the purpose of environment modeling; and how to integrate high-level reasoning and low-level behavior execution. In this volume state-of-the-art fuzzy logic solutions are presented and their pros and cons are discussed in detail based on extensive experimentation on real mobile robots.

Klappentext

In the past decade a critical mass of work that uses fuzzy logic for autonomous vehicle navigation has been reported. Unfortunately, reports of this work are scattered among conference, workshop, and journal publications that belong to different research communities (fuzzy logic, robotics, artificial intelligence, intelligent control) and it is therefore not easily accessible either to the new­ comer or to the specialist. As a result, researchers in this area may end up reinventing things while being unaware of important existing work. We believe that research and applications based on fuzzy logic in the field of autonomous vehicle navigation have now reached a sufficient level of maturity, and that it should be suitably reported to the largest possible group of interested practitioners, researches, and students. On these grounds, we have endeavored to collect some of the most representative pieces of work in one volume to be used as a reference. Our aim was to provide a volume which is more than "yet another random collection of papers," and gives the reader some added value with respect to the individual papers. In order to achieve this goal we have aimed at: . Selecting contributions which are representative of a wide range of prob­ lems and solutions and which have been validated on real robots; and . Setting the individual contributions in a clear framework, that identifies the main problems of autonomous robotics for which solutions based on fuzzy logic have been proposed.

Inhalt

Tutorials: A. Saffiotti: Fuzzy Logic in Autonomous Navigation.- D. Driankov: A Reminder of Fuzzy Logic.- Design of Individual Behaviors: A. Ollero, J. Ferruz, O. Sánchez, G. Heredia: Mobile Robot Path Tracking and Visual Target Tracking Using Fuzzy Logic.- R.R. Murphy: Fuzzy Logic for Fusion of Tactical Influences on Vehicle Speed Control.- J. Godjevac, N. Steele: Neuro-Fuzzy Control for Basic Mobile Robot Behaviours.- F. Hoffmann: The Role of Fuzzy Logic Control in Evolutionary Robotics.- Coordination of Behaviors: F.G. Pin, Y. Watanabe: Resolving Conflicts Between Behaviors Using Suppression and Inhibition.- S.G. Goodridge, M.G. Kay: Multi-Layered Fuzzy Behavior Fusion for Reactive Control of Autonomous Robots.- E.W. Tunstel Jr.: Fuzzy-Behavior Synthesis, Coordination, and Evolution in an Adaptive Behavior Hierarchy.- P. Pirjanian, M. Mataric: Multiple Objective vs. Fuzzy Behavior Coordination.- Mapping the Environment: E. Fabrizi, g. Oriolo, G. Ulivi: Accurate Map Building via Fusion of Laser and Ultrasonic Range Measures.- M. López-Sánchez, R. López de Màntaras, C. Sierra: Map Genaration by Co-operative Autonomous Robots Using Possibility Theory.- J. Gasós: Integrating Linguistic Descriptions and Sensor Observations for the Navigation of of Autonomous Robots.- Layer Integration: H. Surmann, L. Peters: MORIA: A Robot with Fuzzy Controlled Behaviour.- J. Zhang, A. Knoll: Integrating Deliberative and Reactive Strategies via Fuzzy Modular Control.