Mobile Machine Tending with ROS2: Evaluation of system capabilities

Abstract

Automating machine tending for high mix - low volume environment provides additional engineering challenges. Current machine tending systems are usually fixed in place and preprogrammed by a human operator. Available mobile robots are limited in their machine tending capabilities as they cannot provide the required accuracy that is necessary for the precise insertion of work pieces into a fixture. This paper presents a mobile machine tending system that uses computer vision and ROS2 to address these limitations and to enable autonomous machine tending. After a brief introduction to the system architecture, the article provides first insights into the systems capabilities based on experimental evaluation. The first experiment addresses the accuracy of the end effector, showing reasonable improvements using low cost sensors. The second experiment evaluates the network load of the distributed control system and discusses the requirements for the operation of a fleet of such robots.