发表机构
Institute for Smart Electronics and Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学智能电子与系统研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对多机器人协同搬运的通信挑战，在MuJoCo仿真中评估三种控制方法，发现动态采样降开销、轮换领导提公平性且不影响搬运能力，为通信受限场景部署协同机器人提供指导。
AI 中文摘要
执行协同搬运的多机器人团队面临一项核心挑战：在维持稳定控制的同时保证通信高效性。本文研究了如何利用实测网络时延调整自适应采样时间，以及策略性领导者轮换，以在团队中公平分配无线负载。我们在具备真实无线建模的MuJoCo物理仿真环境中，评估三种控制方法：固定采样加静态领导、动态采样加静态领导、动态采样加轮换领导，其中无线建模包含时分多址（TDMA）、介质访问控制（MAC）、抖动、排队及丢包。结果显示存在重要权衡：动态采样可在不损害控制性能的前提下有效降低通信开销，而轮换领导者角色能显著提升空中时间分配的公平性，且对团队搬运能力的影响可忽略不计。据我们所知，本研究是首批在物理真实的多机器人协同场景中，联合探究动态采样、轮换领导与无线协议交互的工作，为在通信资源有限的真实场景中部署协同机器人团队提供了实用指导。
英文摘要
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.
