Certification of Safe and Trusted Robotic Inspection of Assets

In future inspections of offshore assets utilizing robots, robots will not only be expected to collate new data from their payload of instruments, but they will also be expected to interact with the infrastructure being inspected, undertake remedial tasks and engage with embedded monitoring systems of the asset. This increasing level of interaction and deployment frequency of robot inspections requires an understanding of how we can embed safe and trusted operational architectures within robots. Currently, robots can undertake constrained semi-autonomous inspections, using predetermined tasks (missions) with minimum supervision. However, the challenge is that the state of the world changes with time as does the condition of the robot. Therefore, robots must be able to undertake adaptive measures to support optimal outcomes during autonomous missions. In this paper, we propose an initial architecture to the safe verification and validation of health condition and certification of robotic and autonomous inspection systems for offshore assets. Our first contribution relates to the verification and validation architecture, which takes into account risks associated with asset inspection, safety protocols, evolving ambient changes, as well as the inherent state of health of the robot. The second part of our paper looks to how prognostic analytics can be used to support robot resilience in terms of sensor drift and accurate state of health estimates of critical sub-systems. Initial results demonstrate that methods such as relevance vector machines and Bayesian networks can be used to accurately mitigate risks to autonomy.