Energy-optimal Path Planning with Active Flow Perception for Autonomous Underwater Vehicles
Niankai Yang · Dongsik Chang · M. Johnson-Roberson · Jing Sun
TL;DR
An underwater vehicle plans paths that both save energy and gather flow measurements to reduce current-prediction uncertainty, showing energy savings in simulation.
BRIEF
Underwater vehicles that must run for long periods need to know the ocean currents ahead of them, since riding a favorable current saves energy. Currents are hard to predict, though. This work plans paths that do two things at once: use little energy and collect flow measurements that sharpen the vehicle's own current estimates. Uncertainty is quantified with a Cramer-Rao bound on the parameters of a flow model built from historical data. In simulation, the approach is compared with planning using the true flow and with planning using predictions without active perception, and it shows satisfactory energy savings.
Abstract
Accurate flow predictions are critical for energy-optimal path planning of AUVs with endurance requirements. However, the complex dynamics of ocean currents make it difficult to achieve accurate flow predictions. For an AUV with flow and location sensing capabilities, one can optimize vehicle actions so that the flow information collected along the vehicle path reduces flow prediction uncertainty, referred to as active flow perception. In this paper, we propose an energy-optimal path planning approach that incorporates active flow perception. The proposed approach achieves the objectives of vehicle energy consumption minimization and flow prediction uncertainty reduction. To quantify flow prediction uncertainty, an empirical flow model parameterized using the proper orthogonal decomposition (POD) is constructed based on historical data. Assuming negligible unmodeled dynamics in the POD model, the flow prediction uncertainty is evaluated by the Cramer-Rao (CR) bound of estimated model parameters. To establish active flow perception combined with energy optimal path planning, we formulate the cost to be minimized during path planning in terms of vehicle energy using estimated flow parameters and CR bound. Through simulations, the proposed approach is compared with approaches that plan energy-optimal paths using i) true flow and ii) flow predictions without active flow perception. Simulation results demonstrate the satisfactory energy-saving performance of the proposed approach.