A POTENTIAL-FIELD APPROACH TO PATH PLANNING

We present a path-planning algorithm for the classical mover's problem in three dimensions using a potential field representation of obstacles. A potential function similar to the electrostatic potential is assigned to each obstacle, and the topological structure of the free space is derived in the form of minimum potential valleys. Path planing is done at two levels. First, a global planner selects a robot's path from the minimum potential valleys and its orientations along the path that minimize a heuristic estimate of the path length and the chance of collision. Then a local planner modifies the path and orientations to derive the final collision-free path and orientations. If the local planner fails, a new path and orientations are selected by the global planner and subsequently examined by the local planner. This process is continued until a solution is found or there are no paths left to be examined. Our algorithm solves a much wider class of problems than other heuristic algorithms and at the same time runs much faster than exact algorithms (typically 5 to 30 min on a Sun 3/260). The algorithm fails on a small set of very hard problems involving tight free spaces. The performance of our algorithm is demonstrated on a variety of examples.