Intelligent Risk-Based Under-Ice Altitude Control for Autonomous Underwater Vehicles

Abstract

Autonomous underwater vehicles (AUVs) are effective platforms for mapping and monitoring under the sea 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 method for intelligent risk-based under-ice altitude control for AUVs. Firstly, 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 for probabilistic reasoning over the current state of risk during the operation is developed. This network is then extended to a decision network for autonomous risk-based selection and reselection of the setpoint for the altitude controller, balancing the trade-off between the reward of the setpoint and the risk involved. This will improve the system safety and reliability. Results from a simulation study are presented in order to demonstrate the performance of the proposed method.