Robot and Asset Self-Certification

System Certification is a legally mandated, systematic procedure for evaluating, testing, and authorising systems before or during operation. However, inspection for certification in offshore environments is often labour intensive, risky and expensive.

A major obstacle for adopting Robotics and Artificial Intelligence (RAI) for certification is the need to assure systems in terms of their safe operation. While safety and certification procedures have a track record for traditional industrial assets such as oil-drilling rigs, the existing regulatory frameworks do not effectively address the technologies used in RAI systems. This is especially true for self-adaptive or self-learning systems.

Led by Professor David Flynn from Heriot-Watt University, the Robot and Asset Self-Certification team are working closely with ORCA's industry partners and also industry regulators to design RAI systems that can self-certify, diagnose faults and guarantee their safe operation.

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Research areas include:

  • Developing and testing a practical self-certification methodology
  • Certification requirements and techniques for RAI assessment by regulators
  • Certification of self-learning robotic systems
  • Self-diagnosis of faults and self-healing
  • Prognostics for RAI operation reliability

This work theme draws on ORCA'S combined academic expertise in:

  • Autonomous systems
  • Robot verification
  • Asset and condition monitoring
  • Machine learning

Robot & Asset Self-Certification research is being undertaken by:

Institutions involved in this research theme

Did you know?

A crucial output of the project will be working closely with regulators to develop standards and formal requirements for offshore RAI self-certification. This will have a wide impact on the development of formal benchmarks for robots in extreme environments, leading to a step change in the assessment of robotic research in academia.

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