Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms

TL;DR

This paper introduces a novel symbiotic system of systems approach for enhancing safety and resilience in autonomous offshore wind farm robotics, enabling real-time verification and adaptive mission planning.

Contribution

It presents a new methodology and digital architecture for run-time safety, reliability, and resilience verification in autonomous offshore wind farm robotics.

Findings

Successful demonstration of asset inspection mission in confined space

Implementation of a reliability ontology for fault diagnosis

Enhanced decision-making through real-time diagnostics and adaptive planning

Abstract

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to Robotics and Artificial Intelligence (RAI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a Symbiotic System Of Systems Approach (SSOSA), reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our novel methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. To achieve this, a Symbiotic Digital Architecture (SDA) was developed to synchronize digital models of the robot, environment, infrastructure, and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We demonstrate an asset inspection mission within a confined space through Cooperative, Collaborative and Corroborative (C3) governance (internal and external symbiosis) via decision-making processes and the associated structures. We create a hyper enabled human interaction capability to analyze the mission status, diagnostics of critical sub-systems within the robot to provide automatic updates to our AI-driven run-time reliability ontology. This enables faults to be translated into failure modes for decision-making during the mission.