The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

To reduce Operation and Maintenance (OM) 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 (C3), 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, the 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. The 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.

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

In this paper, the results and methodology of a framework to enable run-time safety compliance andself-certification of robotics is presented. This transferable framework is verified within a practicaldemonstration scenario, based on asset inspection within a confined space, and representing a Beyond VisualLine of Sight (BVLOS) use case. The methodology of the framework is based on computationally efficientanalysis to support run-time, front-end, data analysis and adaptive decision-making. Utilizing the HuskyA200 platform, manufactured by Clearpath, front-end datasets on the mission status and diagnostics ofcritical sub-systems within the Husky platform are used to update run-time system ontologies. The holisticand hierarchical-relational model of the robot integrates the automata of the sensed and some non-sensedcomponents, using prior knowledge, such as risk assessments and offline reliability data, to support run-timeanalysis, such as fault prognosis, detection, isolation and diagnosis. These computationally efficient dataand system analyses then enable faults to be translated into failure modes that can affect decision makingduring the mission. With respect to challenges of a dynamic environment, namely ambient conditions orthe presence of unexpected people, Frequency Modulated Continuous Wave (FMCW) sensing is integratedonto the husky platform. The FMCW supports localization in opaque environments and detection of peoplewithin and out-with of the confined space, as well as enabling integrity analysis of the infrastructure. Theframework 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 operationaldecision support and trust for human in the loop operators of varying skill levels. The presentation ofactionable information to the end user is used to support improvements in productivity associated with assetintegrity as well as supporting user trust in safety during a BVLOS mission.