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POTBUG: A mind's eye approach to providing BUG-like guarantees for adaptive obstacle navigation using dynamic potential fields

Research output: ResearchConference contribution



Michael Weir, Anthony Buck, Jon Lewis

School/Research organisations


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.



Original languageEnglish
Title of host publicationFrom Animals to Animats 9
Subtitle of host publication9th International Conference on Simulation of Adaptive Behaviour
EditorsS Nolfi, G Baldassarre, R Calabretta, JCT Hallam, D Marocco, JA Meyer, O Miglino, O Parisi
Number of pages12
ISBN (Electronic)978-3-540-38608-7
ISBN (Print)3-540-38608-4
StatePublished - 2006
Event9th International Conference on Simulation of Adaptive Behaviour, SAB 2006 - Rome, Italy
Duration: 25 Sep 200629 Sep 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Simulation of Adaptive Behaviour, SAB 2006

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