Hierarchical Path Planning of Unmanned Surface Vehicles: A Fuzzy Artificial Potential Field Approach

Abstract

Under unforeseen circumstances, a hierarchical path planning (HPP) scheme combining global and local tasks of an unmanned surface vehicle (USV) is proposed by devising fuzzy artificial potential field (FAPF). Within the HPP scheme, the elite reservation, diversity increment, adaptive mutation probability and adaptive genetic algorithm are incorporated to generate optimally sparse waypoints together with smooth path. To exclusively accommodate unpredictable environments, the innovative FAPF method with fuzzy decision-making is developed to remove singular dilemmas, pertaining to which the obstacles on the path make the USV hesitate. By inserting virtual returning points onto the global path, a hybrid compound combining the global and local paths is established. Finally, the simulations and comparisons in various geographical areas demonstrate the effectiveness and superiority of the proposed HPP scheme.