通过分层分解与迭代优化工作空间，显著提升多机器人路径规划效率。
Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement
- 基于工作空间分解的分层子问题展开策略
- 规划时间最高降低一个数量级，优于传统联合配置空间搜索
- 适合大规模动态环境下的多机器人协同规划场景
多机器人路径规划的核心挑战在于如何在避免机器人间冲突的同时，避免搜索机器人群体联合配置空间带来的巨大计算开销。本文提出一种多移动机器人运动规划方法，通过利用离散搜索工作空间分解来实现机器人间的协调，使规划时间最多提升一个数量级。以往方法依赖工作空间拓扑判断是否需要协调，并将机器人组合进联合配置空间；而本文进一步通过迭代细化工作空间表示，使规划器可在更小、解耦的配置空间中搜索，从而大幅降低计算复杂度。
原文摘要 · Abstract (English)
A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.
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