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This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST)algorithm for the adaptive sampling mission of a hybrid aerial underwatervehicle (HAUV) in an air-sea 3D environment. This algorithm innovativelycombines the tournament-based point selection sampling strategy, theinformation heuristic search process and the framework of Rapidly-exploringRandom Tree (RRT) algorithm. Hence can guide the vehicle to the region ofinterest to scientists for sampling and generate a collision-free path formaximizing information collection by the HAUV under the constraints ofenvironmental effects of currents or wind and limited budget. The simulationresults show that the fast search adaptive sampling tree algorithm has higheroptimization performance, faster solution speed and better stability than theRapidly-exploring Information Gathering Tree (RIGT) algorithm and the particleswarm optimization (PSO) algorithm.

Information-driven Path Planning for Hybrid Aerial Underwater Vehicles