A Bayesian Approach to Risk-Based Autonomy for a Robotic System Executing a Sequence of Independent Tasks

Enabling higher levels of autonomy requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This work presents an approach to improve this ability for a robotic system executing a series of independent tasks, such as inspection, sampling, or intervention, at different locations. A dynamic decision network (DDN) is used to infer the presence of internal faults and the state of the environment by fusing information over time. This knowledge is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Past states are evaluated with new information to identify and counteract previous sub-optimal actions. A case study on an inspection drone tasked with contact-based ultrasound inspection is presented. The case study successfully demonstrates the proposed capabilities while minimizing time use and maximizing mission completion.