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Plan-Based Control of Robotic Agents

Improving the Capabilities of Autonomous Robots

  • Michael Beetz

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2554)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2554)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Pages 1-20
  3. Pages 179-179
  4. Back Matter
    Pages 181-192

About this book

Introduction

Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities.

This book makes three major contributions to improving the capabilities of robotic agents:

 - first, a plan representation method is introduced which allows for specifying flexible and reliable behavior

- second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans

- third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail.

Keywords

Agents Navigation Reactive Control Reactive Plans Robot Control Robot Learning Robot Navigation Robot Perception Robotics Task Management autonom autonomous robot learning proving robot

Editors and affiliations

  • Michael Beetz
    • 1
  1. 1.Institut für Informatik IXTechnische Universität MünchenGarching b. München

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-36381-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-00335-9
  • Online ISBN 978-3-540-36381-1
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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