发表机构
TU Braunschweig; L3S Research Center（布伦瑞克工业大学; L3S研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对异构机器人团队的分散式负载均衡问题，提出基于“接力桶”机制的局部稳定方案，通过令牌调整实现系统收敛，经仿真验证其鲁棒性，可作为复杂场景的基础工具。
AI 中文摘要
我们研究异构机器人群体的分散式自组织任务分配问题，这类机器人需协作完成运输或其他需要协调运动规划的任务。为此，我们提出了关于简单但有效的“接力桶（bucket brigades）”负载均衡机制的理论与实践结果：异构机器人团队在受限的一维空间中共享空间任务，仅能感知与邻居或墙壁的碰撞，目标是在无集中控制或信息的情况下优化系统整体吞吐量，实现与机器人速度成比例的区间划分。我们通过开发基于简单局部辅助（即“令牌（token）”）的稳定机制解决系统可能出现的混沌行为，该机制在机器人相遇后使其暂时减速，这种纯局部调整消除了持续振荡，使系统收敛至稳定状态。我们通过对比单一边界令牌与普遍存在的双向令牌并优化减速因子来加快系统收敛。事件驱动仿真报告了收敛时间与鲁棒性：针对多种扰动（如机器人删除、位置或速度抖动），系统能可靠地重新收敛。结果表明，该局部、实用的机制可为异构机器人团队提供鲁棒负载均衡方案，有望成为更复杂场景的有效工具基础。
英文摘要
We study the problem of decentralized, self-organized task sharing for a swarm of heterogeneous robots that collaborate in transportation or other objectives that require coordinated motion planning. To this end, we present theoretical and practical results for the simple but effective mechanism of \emph{bucket brigades} for load balancing, in which a team of heterogenous robots share a spatial task in a confined, one-dimensional space, while only being able to sense collisions with neighbors or walls. The goal is to optimize throughput of the overall system, without central control or information, aiming at an interval partition proportional to robot velocities. We address possible chaotic system behavior by developing a stabilization mechanism based on simple local aid, a ``token'', that temporarily decelerates robots after an encounter. This purely local change eliminates persistent oscillations, resulting in convergence towards a stable system state. We accelerate system convergence by comparing a single boundary token to ubiquitous two-directional tokens and optimizing the deceleration factor. Event-driven simulations report convergence times and robustness: For a large variety of perturbations (such as robot deletion, position or velocity jittering), the system reliably re-converges. The results suggest a local, practical mechanism for robust load balancing for heterogeneous teams of robots that promises an effective tool as basis for more complex scenarios.
CommentsThis paper was submitted to IROS 2026 on March 2nd and accepted on June 17th
