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.
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:
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.
