Anytime Nonparametric A*

Anytime variants of Dijkstra’s and A* shortest path algorithms quickly produce a suboptimal solution and then improve it over time. For example, ARA* introduces a weighting value (ε) to rapidly find an initial suboptimal path and then reduces ε to improve path quality over time. In ARA*, ε is based on a linear trajectory with ad-hoc parameters chosen by each user. We propose a new Anytime A* algorithm, Anytime Nonparametric A* (ANA*), that does not require ad-hoc parameters, and adaptively reduces ε to expand the most promising node per iteration, adapting the greediness of the search as path quality improves. We prove that each node expanded by ANA* provides an upper bound on the suboptimality of the current-best solution. We evaluate the performance of ANA* with experiments in the domains of robot motion planning, gridworld planning, and multiple sequence alignment. The results suggest that ANA* is as efficient as ARA* and in most cases: (1) ANA* finds an initial solution faster, (2) ANA* spends less time between solution improvements, (3) ANA* decreases the suboptimality bound of the current-best solution more gradually, and (4) ANA* finds the optimal solution faster. ANA* is freely available from Maxim Likhachev’s Search-based Planning Library (SBPL).

Authors

• Jur van den Berg University of Utah
• Rajat Shah University of California, Berkeley
• Arthur Huang University of California, Berkeley
• Ken Goldberg University of California, Berkeley

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