AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC
A new anytime multi-robot motion planning method, AO-ARC, achieves initial solution times on par with state-of-the-art feasibility solvers while converging faster and more reliably as the number of robots grows. It adapts the AO-x meta-algorithm by iteratively calling ARC on bounded instances, proving asymptotic optimality.
[2606.27495] AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC
[Submitted on 25 Jun 2026]
Title:AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC
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Abstract:We present AO-ARC, an anytime multi-robot motion planning (MRMP) method that achieves initial solution times on par with state-of-the-art MRMP feasibility solvers while converging faster and more reliably than existing anytime MRMP methods as the number of robots increases. AO-ARC adapts the AO-x meta-algorithm for converting feasibility solvers into anytime algorithms by iteratively calling the original ARC method on bounded MRMP instances under a makespan cost metric. This exploits the adaptive (de)coupling of ARC while maintaining the consistent cost bound across robot (de)compositions needed for AO-x. We provide theoretical analysis proving the asymptotic optimality properties of AO- ARC and conduct empirical evaluation on a set of 2D scenarios with different levels of coordination complexity and a 3D manipulator scenario representative of real-world applications.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.27495 [cs.RO]
(or arXiv:2606.27495v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.27495
arXiv-issued DOI via DataCite
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From: James Motes [view email] [v1] Thu, 25 Jun 2026 19:20:26 UTC (1,712 KB)
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