AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC

2026-06-25

AI Summary

Multi-robot motion planning faces the challenge of simultaneously achieving fast initial solution generation, efficient optimization of solution quality, and scalability. This work proposes AO-ARC, the first approach to integrate the anytime-optimal AO-x meta-algorithm with an adaptive (de)coupled ARC solver. AO-ARC rapidly produces feasible solutions while guaranteeing asymptotic optimality with respect to makespan and maintaining consistent cost bounds across varying robot decompositions. Experimental results demonstrate that AO-ARC matches state-of-the-art feasibility solvers in initial solution speed and significantly outperforms existing anytime methods in both convergence rate of solution quality and reliability, across 2D coordination scenarios and 3D robotic arm tasks.

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.

Problem

• multi-robot motion planning
• asymptotic optimality
• anytime algorithm
• makespan optimization
• coordination complexity

Innovation

• anytime planning
• asymptotic optimality
• multi-robot motion planning
• adaptive decoupling
• makespan optimization