动态采样与轮换领导提升多机器人协作通信公平性。
TDMA Based Communications Control Co-Design for Cooperative Carrying: Delay Calibration and Sampling-Rate Optimization
- 根据网络延迟自适应调整采样时间，减轻通信负担。
- 轮换领导者使无线资源分配更公平，不影响搬运能力。
- 在真实物理仿真中验证，适合资源受限场景部署。
执行协作搬运的多机器人团队面临核心挑战：在保持稳定控制的同时维持高效通信。本文研究了基于测量网络延迟的自适应采样时间调整与策略性领导者轮换如何公平分配团队间的无线负载。我们在 MuJoCo 中使用基于物理的仿真，集成时分多址（TDMA）、介质访问控制、抖动、队列和丢包等真实无线特性，评估三种控制方法：固定采样+静态领导、动态采样+静态领导、动态采样+轮换领导。结果表明：动态采样可有效降低通信开销且不损害控制性能；轮换领导显著改善空口时间分配公平性，对团队搬运能力影响可忽略。据我们所知，这是首个联合考察动态采样、轮换领导与无线协议交互的物理真实多机器人协作工作，为现实环境中通信资源受限的协同机器人部署提供实用指导。
原文摘要 · Abstract (English)
Multi robot teams performing cooperative transportation face a fundamental challenge: maintaining stable control while keeping communications efficient. This paper investigates how adaptive sampling time adjustment informed by measured network delay and strategic leader rotation can distribute wireless load fairly across the team. We use physics based simulation in MuJoCo with realistic wireless modeling, including time division multiple access, medium access control, jitter, queueing, and packet loss, to evaluate three control approaches: fixed sampling with static leadership, dynamic sampling with static leadership, and dynamic sampling with rotating leadership. Our results reveal an important trade off: dynamic sampling effectively reduces communications overhead without compromising control performance, while rotating the leader role meaningfully improves how fairly airtime is distributed all with negligible impact on the team carrying ability. to the best of our knowledge, being among the first to jointly examine dynamic sampling, rotating leadership, and wireless protocol interactions in physicsrealistic multi robot cooperation, this work provides practical guidance for deploying coordinated robotic teams in real world settings where communications resources are limited.
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