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POTBUG: A Mind’s Eye Approach to Providing BUG-Like Guarantees for Adaptive Obstacle Navigation Using Dynamic Potential Fields

  • Michael Weir
  • Anthony Buck
  • Jon Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)

Abstract

The problem we address is adaptive obstacle navigation for autonomous robotic agents in an unknown or dynamically changing environment with a 2-D travel surface without the use of a global map. Two well known but hitherto apparently antithetical approaches to the problem, potential fields and BUG algorithms, are synthesised here. The best of both approaches is attempted by combining a Mind’s Eye with dynamic potential fields and BUG-like travel modes. The resulting approach, using only sensed goal directions and obstacle distances relative to the robot, is compatible with a wide variety of robots and provides robust BUG-like guarantees for successful navigation of obstacles. Simulation experiments are reported for both near-sighted (POTBUG) and far-sighted (POTSMOOTH) robots. The results are shown to support the theoretical design’s intentions that the guarantees persist in the face of significant sensor perturbation and that they may also be attained with smoother paths than existing BUG paths.

Keywords

Potential Field Sensor Range Travel Path Obstacle Distance Goal Direction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Weir
    • 1
  • Anthony Buck
    • 1
  • Jon Lewis
    • 1
  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsScotland

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