Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

Abstract

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

“…Considering the state uncertainty due to the ocean currents, the work in [104] presented an energy-aware feedback planning method for an LRAUV. It uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goalconstrained belief space to make the feedback plan synthesis computationally tractable.…”

Time-Optimal Path Planning of a Hybrid Autonomous Underwater Vehicle Based on Ocean Current Neural Point Grid

“…In order to solve the problem of dynamic path planning of the environmental monitoring USV in complex sea areas effectively, a hybrid algorithm is presented via combining the global path planning with local path planning [34]. Considering the state uncertainty during the random energy sensing path planning, an energy sensing feedback planning method is proposed to deal with the motion uncertainty in the LRAUV kinematic model, as well as the sensor noise [35]. To facilitate the vessel navigation in ocean current and the dynamic scenes with multiple marine vehicles, a time-optimal path for a marine vehicle is introduced [36][37][38].…”