Optimal Path Planning Method for Unmanned Surface Vehicles Based on Improved Shark-Inspired Algorithm

Abstract

As crucial technology in the auto-navigation of unmanned surface vehicles (USVs), path-planning methods have attracted scholars’ attention. Given the limitations of White Shark Optimizer (WSO), such as convergence deceleration, time consumption, and nonstandard dynamic action, an improved WSO combined with the dynamic window approach (DWA) is proposed in this paper, named IWSO-DWA. First, circle chaotic mapping, adaptive weight factor and the simplex method are used to improve the initial solution and spatial search efficiency and accelerate the convergence of the algorithm. Second, optimal path information planned by the improved WSO is put into the DWA to enhance the USV’s navigation performance. Finally, the COLREGs rules are added to the global dynamic optimal path planning method to ensure the USV’s safe navigation. Compared with the WSO, the experimental simulation results demonstrate that the path length cost, steering cost and time cost of the proposed method are decreased by 13.66%, 18.78% and 79.08%, respectively, and the improvement in path smoothness cost amounts to 19.85%. Not only can the proposed IWSO-DWA plan an optimal global navigation path in an intricate marine environment, but it can also help a USV avoid other ships dynamically in real time and meets the COLREGs rules.