Modeling and Validation of Quality of Control for Edge-Offloaded Collaborative Navigation
For roboticists and network engineers designing collaborative navigation systems, this work provides validated models and optimal co-design regimes, though it is an incremental extension of prior QoC frameworks.
This paper addresses the challenge of stochastic wireless delay and reliability in edge-offloaded collaborative navigation by expanding the quality of control (QoC) framework to practical robotic models. Experimental validation on a private 5G testbed shows that RELIABLE QoS offers 51.5% better QoC than BEST-EFFORT under certain settings.
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.