Runtime Energy Estimation and Route Optimization for Autonomous Underwater Vehicles

This paper is focused on improving the self-awareness of autonomous underwater vehicles (AUVs) operating in unknown environments. A runtime estimation framework is introduced to derive energy usage and navigation performance metrics in the presence of external disturbances, such as slowly varying sea currents. These are calculated by a state-of-the-art nonlinear regression algorithm (LWPR) using measurements commonly available on-board modern AUVs without relying on external sensors or a priori knowledge about the environment. The proposed framework is validated on two vehicles, an IVER3 AUV and a Nessie VII AUV, in the context of real sea trials with no modification required for the AUVs or their missions. Derived metrics are used to estimate the feasibility of underwater missions employing the concept of probability of mission completion (PoMC). If environmental effects modify the vehicle's effectiveness, a mission plan update is performed. This is based on an energy-aware route optimization algorithm that is also introduced in the paper. This algorithm, known as energy-aware orienteering problem (EA-OP), shows a practical usage for the runtime metrics. It allows an AUV to optimize its navigation and to maximize its mission's outcome according to measured performances. Simulation results are also presented for inspection scenarios. These show average improvements of 5%–20% for the mission's outcome when using the proposed strategy in the presence of environmental disturbances.