Information-Driven Path Planning for Hybrid Aerial Underwater Vehicles

This article presents a novel rapidly-exploring adaptive sampling tree algorithm for adaptive sampling missions using a hybrid aerial underwater vehicle (HAUV) in an air-sea 3-D environment. This algorithm innovatively combines the tournament-based point selection sampling strategy, the information heuristic search process, and the framework of the rapidly-exploring random tree algorithm. Hence, the vehicle can be guided to a region of interest to scientists for sampling and generate a collision-free path for maximizing information collection by the HAUV under the constraints of environmental effects of currents or wind and a limited budget. The simulation results show that the fast search adaptive sampling tree algorithm has higher optimization performance, faster solution speed, and better stability than the rapidly-exploring information gathering tree algorithm and the particle swarm optimization algorithm.