Enhancing the application of the grey wolf optimizer algorithm in USV path planning: a research study

Unmanned Surface Vessel (USV) is autonomous waterborne carriers with the need for the capability to interact with the external environment. To achieve this objective, USV must possess the abilities for path planning and dynamic obstacle avoidance to address various potential hazardous situations. During the navigation of USV, not only is global path planning decision-making necessary, but timely responses to local hazardous environments are also crucial to prevent accidents. Only through these measures can USV ensure the safe, efficient, and smooth completion of tasks. Building upon the foundation of the USV motion model, this paper identifies and summarizes the shortcomings of the Grey Wolf Optimizer (GWO) algorithm, proposing improvement strategies. A GWO algorithm based on random walks is introduced, utilizing random exploration of the search space through random walks, followed by having omega (ω) wolves follow them to update their positions, thereby enhancing global search capabilities. Through comparative algorithm simulations, the refined algorithm has made significant strides, demonstrating faster convergence and improved effectiveness.