Distributed Control for Collaborative Robotic Systems Using 5G Edge Computing

Abstract

Collaborative and mobile robotics for industrial environments promise to enable autonomous and flexible production processes. However, this vision also poses significant challenges to the robotic systems, requiring them to adapt to dynamic environments and ensure human safety by leveraging continuous image streams and sophisticated data processing. Edge computing allows offloading the computational load to edge servers, communicating the image data, generated on mobile robots, over fast and reliable private 5G networks. However, there are multidisciplinary and interdependent factors that influence the reaction time of the distributed system which are not well explored in the literature for real robotic use cases, but have a significant impact on safe robotic behavior and the effectiveness of edge computing. In this work, we implement a distributed control system that offloads the image processing to measure and analyze the effect of various factors on the reaction time of the system for collaborative robotics applications. Different values for the sensing rate, image resolution and compression and quality-of-service settings are evaluated for communication and computation times as well as for the task performance. To account for the safety requirements in collaborative robotics, we add a low-level control timeout in cases of large jitter and stop the robot in cases of frequently missed detections. A push and a teleoperation experiment evaluate the reaction times in real distributed control scenarios using 5G edge computing. All experiments are implemented using ROS 2 Humble. The code and videos of the experiments are available at https://github.com/DominikUrbaniak/ros2_distributed_control_system.

Keywords

• 5G
• collaborative robotics
• distributed control
• edge computing
• image processing
• Industry 4.0