Anytime Nonparametric A*

Author:

Van den Berg Jur,Shah Rajat,Huang Arthur,Goldberg Ken

Abstract

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 "epsilon" to rapidly find an initial suboptimal path and then reduces "epsilon" to improve path quality over time. In ARA*, "epsilon" 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 varepsilon 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).

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Path Planning for Autonomous Inland Vessels in Complex Harbor Environments;2023 21st International Conference on Advanced Robotics (ICAR);2023-12-05

2. Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain;IEEE Transactions on Robotics;2023-06

3. Informable Multi-Objective and Multi-Directional RRT* System for Robot Path Planning;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

4. Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps;The International Journal of Robotics Research;2023-04

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