Data uncertainty management in path planning for underwater ocean gliders

The special characteristics of the ocean gliders propulsion scheme endows these vehicles with an extremely high endurance, at the cost of a relatively low surge speed. Hence, gliders' trajectory is highly influenced by ocean currents, which can even exceed the vehicle's nominal velocity. This calls for intelligent path planning algorithms, fact that reflects in the high number of solutions proposed by researchers that can be found in recent bibliography. In this paper we propose a novel method to combine multiple ocean forecast models in order to feed the glider path-planner with as reliable as possible source information. The scheme is configured as a multi-evidence fusion that integrates factors such as sensor vs model validation, forecast stability and model historic performance.