To reduce Operation and Maintenance (O&M) expenditure on offshore wind farms, wherein 80% of the cost relates to deploying personnel, the offshore wind sector looks to advances in Robotics and Artificial Intelligence (RAI) for solutions. Barriers to residential Beyond Visual Line of Sight (BVLOS) autonomy as a service, include operational challenges in run-time safety compliance, reliability and resilience, due to the complexities of dealing with known and unknown risk in dynamic environments. In this paper we incorporate a Symbiotic System Of Systems Approach (SSOSA) that uses a Symbiotic Digital Architecture (SDA) to provide a cyber physical orchestration of enabling technologies. Implementing a SSOSA enables Cooperation, Collaboration and Corroboration (C 3 ), as to address run-time verification of safety, reliability and resilience during autonomous missions. Our SDA provides a means to synchronize distributed digital models of the robot, environment and infrastructure. Through the coordinated bidirectional communication network of the SDA, the remote human operator has improved visibility and understanding of the mission profile. We evaluate our SSOSA in an asset inspection mission within a confined operating environment. Demonstrating the ability of our SSOSA to overcome safety, reliability and resilience challenges. The SDA supports lifecycle learning and co-evolution with knowledge sharing across the interconnected systems. Our results evaluate both sudden and gradual faults, as well as unknown events, that may jeopardize an autonomous mission. Using distributed and coordinated decision making, SSOSA enhances the analysis of the mission status, which includes diagnostics of critical sub-systems within the resident robot. This evaluation demonstrates that the SSOSA provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. SSOSA has the potential to be a highly transferable methodology to other mission scenarios and technologies, providing a pathway to implementing scalable autonomy as a service.

In this paper, the results and methodology of a framework to enable run-time safety compliance and self-certification of robotics is presented. This transferable framework is verified within a practical demonstration scenario, based on asset inspection within a confined space, and representing a Beyond Visual Line of Sight (BVLOS) use case. The methodology of the framework is based on computationally efficient analysis to support run-time, front-end, data analysis and adaptive decision-making. Utilizing the Husky A200 platform, manufactured by Clearpath, front-end datasets on the mission status and diagnostics of critical sub-systems within the Husky platform are used to update run-time system ontologies. The holistic and hierarchical- relational model of the robot integrates the automata of the sensed and some non-sensed components, using prior knowledge, such as risk assessments and offline reliability data, to support run-time analysis, such as fault prognosis, detection, isolation and diagnosis. These computationally efficient data and system analyses then enable faults to be translated into failure modes that can affect decision making during the mission. With respect to challenges of a dynamic environment, namely ambient conditions or the presence of unexpected people, Frequency Modulated Continuous Wave (FMCW) sensing is integrated onto the husky platform. The FMCW supports localization in opaque environments and detection of people within and out-with of the confined space, as well as enabling integrity analysis of the infrastructure. The framework presents its results within a symbiotic digital twin of the infrastructure and robotic platform. With fully synchronized communication and data streams, the interactive digital twin provides operational decision support and trust for human in the loop operators of varying skill levels. The presentation of actionable information to the end user is used to support improvements in productivity associated with asset integrity as well as supporting user trust in safety during a BVLOS mission.

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