arXiv cs.MA· Shashank A. Deshpande, Blake T. Edwards, Jonathan P. How·· 13 小时前AI 评分31
WarpWeaver：多机器人协调的分散式树协议
Dispersive Tree Agreements for Multi-Robot Coordination
AI 导读
WarpWeaver 是一种分散式实时规划器，通过分散式列生成让机器人就共享空间协商租金，以任意时间过程逼近联合最优，并在静止时认证近优性。在 Dynobench 仿真中，其总行程时间较集中式离线规划器最多减少 22%，较分散式实时基线最多减少 37%，并将图连通性任务专用规划器的成功率提升近一倍。六台机器人的机载硬件实验验证了其实际可部署性。
正文 · 原文
Abstract:Multi-robot coordination of nonlinear systems is naturally formulated as joint-space optimal control subject to constraints over groups of robots. Existing methods that approach joint optimality remain largely centralized, offline, and confined to collision-free motion planning, while decentralized, real-time counterparts target safe navigation without pursuing the joint optimum. This paper develops a decentralized, real-time planner that approaches the joint optimum as an anytime process. We extend a recent single-robot framework that searches dispersive trees of forward-propagated commands for differentially flat systems to coordinate a multi-robot team near-optimally. We first show that the inter-robot coupling can be effectively replaced by some local constraints, under which locally grown dispersive trees contain a near-optimal agreement. It is this structure that the robots pursue by decentralized column generation, negotiating rents on the shared space that guide the search of each robot's tree. The negotiation settles on an agreement whose near-optimality is certified at quiescence. Our protocol, WarpWeaver, implements this negotiation as an asynchronous real-time loop over a receding horizon, and demonstrates effective closure to quiesced agreements even when real-time deadlines cut the negotiation short. In simulation, WarpWeaver reduces total travel time by up to 22% against centralized offline planners on the Dynobench suite and by up to 37% against a decentralized real-time baseline, and nearly doubles the success rate of a task-specific planner on graph connectivity. Hardware experiments on six robots planning onboard demonstrate real-world implementability.
| Comments: | 19 pages, 5 figures, 7 tables. Submitted to IEEE Transactions on Robotics |
| Subjects: | Robotics (cs.RO); Multiagent Systems (cs.MA); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.31540 [cs.RO] |
| (or arXiv:2610.31540v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31540 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shashank Deshpande Mr [view email]
[v1]
Thu, 22 Oct 2026 16:48:09 UTC (2,036 KB)
来源：arXiv cs.MA · arxiv.org