The edge architecture for semi-autonomous industrial robotic inspection systems

Abstract

Robots have been increasingly used in industrial applications, being deployed along other robots and human supervisors in the automation of complex tasks such as the inspection, monitoring and maintenance of industrial assets. In this paper, we shared our experience and presented our implemented software framework for such edge computing for semi-autonomous robotics inspection. These systems combine human-in-the-loop, semi-autonomous robots, edge computing and cloud services to achieve the automation of complex industrial tasks. This paper describes a robotic platform developed, discussing the key architectural aspects of a semi-autonomous robotics system employed in two industrial inspection case studies: remote methane detection in oilfields, and flare stack inspections in oil and gas production environment. We outline the requirements for the system, sharing the experience of our design and implementation trade-offs. In particular, the synergy among the semi-autonomous robots, human supervisors, model-based edge controls, and the cloud services is designed to achieve the responsive onsite monitoring and to cope with the limited connectivity, bandwidth and processing constraints in typical industrial setting.