Abstract
This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation.
The main contribution of this article is to demonstrate how methods from the risk sciences can be combined with methods from the field of artificial intelligence to design risk awareness into automated systems. More precisely a system theoretic process analysis (STPA) is performed to identify how the system can fail. The results of the STPA are used to build a Dynamic Decision Network (DDN), which is used as an online risk model. An optimization algorithm then uses the online risk model to find the optimal parameter configuration that ensures an acceptable risk level.
A case study of a tethered industrial inspection drone is considered. Through experimental trials, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it uses this information to modify maximum speed, safety distance, and maximum vertical acceleration such that the risk level remains acceptable. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator.
The main contribution of this article is to demonstrate how methods from the risk sciences can be combined with methods from the field of artificial intelligence to design risk awareness into automated systems. More precisely a system theoretic process analysis (STPA) is performed to identify how the system can fail. The results of the STPA are used to build a Dynamic Decision Network (DDN), which is used as an online risk model. An optimization algorithm then uses the online risk model to find the optimal parameter configuration that ensures an acceptable risk level.
A case study of a tethered industrial inspection drone is considered. Through experimental trials, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it uses this information to modify maximum speed, safety distance, and maximum vertical acceleration such that the risk level remains acceptable. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator.