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.

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Accurate underwater navigation systems are required for closed-loop guidance and control of unmanned underwater vehicles (UUV). This paper proposes a sensor-based hybrid translational observer concept for underwater navigation using the hybrid dynamical systems framework, accounting for noisy, asynchronous and sporadic sensor measurements. Sensor measurements from an acoustic positioning system, a Doppler Velocity Log (DVL), an Inertial Measurement Unit (IMU) and a pressure gauge are used in the proposed observer. A method for filtering high-frequency noise is proposed, where the estimated states are obtained by taking a weighted discounted average of a finite number of previous measurements predicted forwards to the current time. The attitude of the vehicle is assumed known, and the acceleration measurements are assumed to be continuously available. Measurements of position, depth and linear velocity are assumed to be asynchronous and sporadically available, that is, they do not arrive at the same time, and their sampling rates are not constant. Uniform global asymptotic stability (UGAS) is established using Lyapunov theory for hybrid systems. Results from simulations are presented in order to demonstrate the performance of the proposed method.

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