Adaptive bearing sampling for a constant-time surfacing A* path planning algorithm for gliders

Abstract

Unmanned Underwater Vehicles (UUVs) are commonly used in Oceanography due to their relative low cost and wide range of capabilities. Gliders are a type of UUV particularly suitable for long-range missions because of their large autonomy. They change their buoyancy to dive and climb describing a vertical saw tooth pattern, which produces an effective but low horizontal speed. Consequently, gliders are strongly sensitive to ocean currents, so they might have to adapt the heading to the current field. In this article we outline a novel path planning algorithm for gliders using ocean currents. It bases on the A* family of algorithms and incorporates a probabilistic framework. Our approach intends to alleviate some of the drawbacks that A* has with the problem at hand. Instead of discretizing the search space, a set of bearing angles is sampled at each surfacing point and the glider trajectory is integrated. We propose an Adaptive Bearing Sampling (ABS) procedure which reduces the computational time with low impact on the results, as shown by the tests run with ocean currents of a Regional Ocean Model.