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Communication Dans Un Congrès Année : 2020

Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space

Résumé

We present a novel analysis of AO-RRT: a treebased planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores the state-cost space and has been shown to efficiently obtain high-quality solutions in practice without relying on the availability of a computationally-intensive twopoint boundary-value solver. Our main contribution is an optimality proof for the single-tree version of the algorithm-a variant that was not analyzed before. Our proof only requires a mild and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying these assumptions, any trajectory having a piecewise-constant control function and positive clearance from the obstacles can be approximated arbitrarily well by a trajectory found by AO-RRT. We also discuss practical aspects of AO-RRT and present experimental comparisons of variants of the algorithm.
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hal-03467674 , version 1 (06-12-2021)

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Michal Kleinbort, Edgar Granados, Kiril Solovey, Riccardo Bonalli, Kostas E. Bekris, et al.. Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space. 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, Paris, France. pp.6344-6350, ⟨10.1109/ICRA40945.2020.9197236⟩. ⟨hal-03467674⟩

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