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

Daniel Mitchell*, Jamie Blanche, Osama Zaki, Joshua Roe, Leo Kong, Samuel Harper, Valentin Robu, Theodore Lim, David Flynn

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)141421-141452
Number of pages32
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Artificial intelligence
  • autonomous systems
  • digital twin
  • non-destructive evaluation
  • resilient robotics
  • safety compliance
  • sensors
  • symbiotic systems
  • system ontology

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