An efficient RRT-based motion planning algorithm for autonomous underwater vehicles under cylindrical sampling constraints

Quickly finding high-quality paths is of great significance for autonomous underwater vehicles (AUVs) in path planning problems. In this paper, we present a cylinder-based heuristic rapidly exploring random tree (Cyl-HRRT*) algorithm, which is the extension version of the path planner presented in our previous publication. Cyl-HRRT* increases the likelihood of sampling states that can improve the current solution by biasing the states into a cylindrical subset, thus providing better paths for AUVs. A direct greedy sampling method is proposed to explore the space more efficiently and accelerate convergence to the optimum. To reasonably balance the optimization accuracy and the number of iterations, a beacon-based adaptive optimization strategy is presented, which adaptively establishes a cylindrical subset for the next focused sampling according to the current path. Furthermore, the Cyl-HRRT* algorithm is shown to be probabilistically complete and asymptotically optimal. Finally, the Cyl-HRRT* algorithm is comprehensively tested in both simulations and real-world experiments. The results reveal that the path generated by the Cyl-HRRT* algorithm greatly improves the power savings and mobility of the AUV.