POTBUG: A Mind's Eye Approach to Providing BUG-like Guarantees For Adaptive Obstacle Navigation Using Dynamic Potential Fields
Michael Weir, Anthony Buck and Jon Lewis
Simulation of Adaptive Behavior 2006 (SAB 2006)
Rome, Italy, 25-29 September 2006
Summary
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. 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 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 farsighted (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.