Path Planning for Unmanned Surface Vehicles Based on Modified Artificial Fish Swarm Algorithm with Local Optimizer

Abstract

The appeal for safe navigation of autonomous surface vessels (ASVs) has deemed the path planning problem as an attractive research interest. However, most of the previous works to solve the path planning problem focus on finding the shortest and collision-free path, but the solutions are scarcely satisfied by the safety requirements and constraints related to the USVs' mechanical systems. To address this challenge, we present a novel path planning method based on a modified artificial fish swarm algorithm in combination with a path optimizer. The modifications are made from two perspectives: (1) Four customized operators and an adaptive factor are applied to improve the convergence performance of the algorithm. (2) A local path optimizer is proposed to enhance its feasibility of cooperating with the USV control system. Path safety, path smoothness, and nonholonomic constraints are considered. The path planning benchmark experiments have demonstrated its superior performance in terms of efficiency and path quality compared to other state-of-the-art algorithms. Moreover, the proposed method is also integrated into the USV's control system in a practical environment with satisfactory feasibility. The simulation results provide strong evidence that the proposed method can be regarded as a practical approach for USV path planning problems.