A Bayesian Approach to Supervisory Risk Control of AUVs Applied to Under-Ice Operations

Autonomous underwater vehicles (AUVs) are efficient sensor-carrying platforms for mapping and monitoring undersea ice. However, under-ice operations impose demanding requirements to the system, as it must deal with uncertain and unstructured environments, harsh environmental conditions, and reduced capabilities of the navigational sensors. This paper proposes a Bayesian approach to supervisory risk control, with the objective of providing risk management capabilities to the control system. First, an altitude guidance law for following a contour of an ice surface via pitch control using measurements from a Doppler velocity log (DVL) is proposed. Furthermore, a Bayesian network (BN) for probabilistic reasoning over the current state of risk during the operation is developed. This is then extended to a decision network (DN) for autonomously adapting the behavior of the AUV in order to maximize the mission utility, subject to a constraint on the predicted risk from the risk model. The vehicle is thus able to autonomously adapt its behavior in response to its current belief about the risk. The goal of this work is to improve the AUV performance and likelihood of mission success. Results from a simulation study are presented in order to demonstrate the performance of the proposed method.