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.