Video: Self-Certification and Safety Compliance for Robotics Platforms
- Osama Farouk Zaki (Heriot-Watt University) | David Flynn (Heriot-Watt University) | Jamie Rowland Douglas Blanche (Heriot-Watt University) | Joshua Kenneth Roe (Heriot-Watt University) | Leo Kong (Heriot-Watt University) | Daniel Mitchell (Heriot-Watt University) | Theo Lim (Heriot-Watt University) | Sam Thomas Harper (Heriot-Watt University) | Valentin Robu (Heriot-Watt University)
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- Offshore Technology Conference
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- 2020. Copyright is retained by the author. This document is distributed by OTC with the permission of the author. Contact the author for permission to use material from this document.
- 7.1.8 Asset Integrity, 7.2 Risk Management and Decision-Making, 6.3 Safety, 7 Management and Information, 7.1 Asset and Portfolio Management, 7.2.1 Risk, Uncertainty and Risk Assessment
- Self-Certification, Reliability, Safety Compliance, BVLOS, Offshore
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In this paper, the results and methodology of a framework to enable run-time safety compliance and self-certification of robotics is presented. This transferable framework is verified within a practical demonstration scenario, based on asset inspection within a confined space, and representing a Beyond Visual Line of Sight (BVLOS) use case. The methodology of the framework is based on computationally efficient analysis to support run-time, front-end, data analysis and adaptive decision-making. Utilizing the Husky A200 platform, manufactured by Clearpath, front-end datasets on the mission status and diagnostics of critical sub-systems within the Husky platform are used to update run-time system ontologies. The holistic and hierarchical- relational model of the robot integrates the automata of the sensed and some non-sensed components, using prior knowledge, such as risk assessments and offline reliability data, to support run-time analysis, such as fault prognosis, detection, isolation and diagnosis. These computationally efficient data and system analyses then enable faults to be translated into failure modes that can affect decision making during the mission. With respect to challenges of a dynamic environment, namely ambient conditions or the presence of unexpected people, Frequency Modulated Continuous Wave (FMCW) sensing is integrated onto the husky platform. The FMCW supports localization in opaque environments and detection of people within and out-with of the confined space, as well as enabling integrity analysis of the infrastructure. The framework 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 operational decision support and trust for human in the loop operators of varying skill levels. The presentation of actionable information to the end user is used to support improvements in productivity associated with asset integrity as well as supporting user trust in safety during a BVLOS mission.