Sea-Air Unmanned Platform Path Planning for an Underwater Target Search-and-Track via IPSO Algorithm

Heterogeneous unmanned maritime systems can significantly enhance the efficiency of completing complex tasks. In this paper, a Sea-Air Unmanned Platform composed of Unmanned Aerial Vehicle (UAV), Unmanned Surface Vehicle (USV), and Autonomous Underwater Vehicle (AUV) is employed to perform the search and tracking of unknown underwater targets in offshore waters. First, a model for the Underwater Target Search-and-Track Mission is constructed, with the task divided into two phases: target search and target tracking. The objectives of these two phases are to maximize the total search space and minimize the terminal position error between the AUV and the underwater target, respectively. Then, a cross-domain collaborative search model for the Sea-Air Unmanned Platform is established, incorporating constraints such as the maneuverability limits and communication ranges of the heterogeneous unmanned vehicles. Finally, based on the traditional Particle Swarm Optimization algorithm, a compression strategy and elite retention strategy are introduced to generate paths for the search and tracking phases in a centralized and distributed manner, respectively. Simulation experiments demonstrate that the Sea-Air Unmanned Platform, utilizing the improved PSO algorithm(IPSO), can efficiently complete the Underwater Target Search-and-Track Mission.