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

摘要

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 nearsighted (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.