Abstract:We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or linearizable dynamics evolving in workspace with arbitrary-shaped obstacles subject to state and control constraints. Safety requirements are formulated as chance-constraints. Our approach leverages data from trajectories of the system to learn a Wasserstein ambiguity tube, i.e., a sequence of ambiguity sets, which contains the trajectory of the system's state distribution with high confidence. This ambiguity tube is then used in a probabilistically complete algorithm to grow a sampling-based motion planning tree that respects the constraints of the problem. We show that learning several lower-dimensional ambiguity tubes instead of a single high-dimensional one effectively reduces the conservatism and boosts scalability. Additionally, we design an efficient bandit-based validity checker that remarkably increases the empirical performance of our approach without sacrificing probabilistic completeness. Case studies show our algorithm finds valid plans in cluttered environments under strict safety thresholds, outperforming state-of-the-art methods.
| Subjects: | Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.26625 [cs.RO] |
| (or arXiv:2605.26625v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26625 arXiv-issued DOI via DataCite (pending registration) |
From: Ibón Gracia [view email]
[v1]
Tue, 26 May 2026 07:02:27 UTC (2,772 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。