  This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST)
algorithm for the adaptive sampling mission of a hybrid aerial underwater
vehicle (HAUV) in an air-sea 3D environment. This algorithm innovatively
combines the tournament-based point selection sampling strategy, the
information heuristic search process and the framework of Rapidly-exploring
Random Tree (RRT) algorithm. Hence can guide the vehicle to the 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 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 (RIGT) algorithm and the particle
swarm optimization (PSO) algorithm.
