多机器人路径规划新方法，快且更可靠。
AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC
- 用迭代调用原算法的方式，将可行性求解器转为可随时优化的算法。
- 在机器人数量增加时，收敛速度和成功率优于现有方法。
- 适合需要高可靠性与实时优化的多机协同场景。
我们提出AO-ARC，一种任意时间多机器人运动规划（MRMP）方法，在初始解生成速度上与当前最先进的可行性求解器相当，同时在机器人数量增加时，收敛更快且更可靠。AO-ARC通过迭代调用原始的ARC方法，在有限规模的MRMP实例上以完成时间（makespan）为代价指标，适配了AO-x元算法，从而利用了ARC的自适应解耦机制，并保持了跨机器人解耦中的一致代价边界。我们提供了理论分析，证明了AO-ARC的渐近最优性，并在不同协调复杂度的2D场景及代表真实应用的3D机械臂场景中进行了实证评估。
原文摘要 · Abstract (English)
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.
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