AI 中文总结
针对复杂环境中协同控制受网络问题挑战，本文扩展控制质量框架到实际机器人模型，建模网络影响、探索控制参数影响并实验验证，得出最优操作模式，还比较了ROS~2中QoS策略的QoC性能。
AI 中文摘要
复杂环境中的协同控制受到随机无线延迟和可靠性变化的严峻挑战，这会降低导航、跟踪和避撞能力。这些网络诱导的不确定性使协同任务期间的能源效率维护变得复杂，并可能导致资源过度配置。本文针对具有动态避撞的导航设置，通过将控制质量（QoC）框架从先前工作扩展到实际机器人模型来应对这一挑战。我们的方法：（i）对端到端网络对闭环性能的影响进行建模；（ii）系统地探索各种控制参数对机器人运动的影响；（iii）通过在私有5G测试平台上进行实验验证这些模型。我们的分析指出了实际机器人的最优控制-通信协同设计操作模式，并比较了标准ROS~2服务质量（QoS）策略在实际条件下的QoC性能，表明在某些实验设置下，可靠QoS比尽力而为QoS的QoC性能好51.5%。
英文摘要
Collaborative control in complex environments is severely challenged by stochastic wireless delay and reliability variations, which can degrade navigation, tracking, and collision avoidance. These network-induced uncertainties complicate the maintenance of energy efficiency during collaborative tasks, and can potentially lead to over-provisioning of resources. In this paper, for a navigation setup with dynamic collision avoidance, we address this challenge by expanding the quality of control (QoC) framework from prior works to practical robotic models. Our approach (i) models end-to-end network effects on closed-loop performance, (ii) systematically explores the impact of various control parameters dictating robotic motion on network latency-reliability (iii) validates these models through experiments on a private 5G testbed across varying delay, reliability and control configurations. Our analysis indicates the optimal control-communication co-design operating regimes for practical robots and also compares the QoC performance of standard ROS~2 quality of service (QoS) policies under real-world conditions and showing how RELIABLE QoS offers 51.5% better QoC than BEST-EFFORT under certain experimental settings.
CommentsAccpeted in IEEE VTC-Fall 2026
