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Probabilistic path planning driven by a potential field is a well established technique and has been successfully exploited to solve complex problems arising in a variety of domains. However, planners implementing this approach are rather inefficient in dealing with certain types of local minima occurring in the potential field, especially those characterized by deep or large attraction basins. In this paper, we present a potential field planner combining "smart" escape motions from local minima with parallel computation to improve overall performance. The results obtained show significant improvement in planning time, along with remarkable reduction in standard deviation. A performance comparison on a benchmark problem of the potential field planner and an existing, state-of-the-art planner is also included. Our investigation confirms the effectiveness of potential field as heuristic to solve difficult path planning problems.
Caselli et al. (2003) studied this question.