Cooperative Search Path Planning for Multiple Unmanned Surface Vehicles

Due to the advantages of unmanned surface vehicles (USVs) with wide communication range, faster speed and high flexibility, USV formations are considered suitable for fast target search missions in marine environments, but the problem of how to exploit the clustering advantages of USV formations is still to be studied. In this paper, the multiple homogeneous USV cluster system collaborative search path planning for the underwater moving target is studied, and an improved genetic algorithm based on particle swarm optimization strategy is used for the path planning of the USV system. Firstly, a new concept of credible period interval is added to the USV search process for distinguishing high-security and low-security regions, and then the objectives of information sharing among USVs and joint search area of USVs are added to the fitness function to guide multiple USVs to collaborate more efficiently in performing search tasks. Comparative experiments demonstrate that the multi-USV cluster system collaboration can complete search tasks more quickly than the equivalent number of USV platforms implementing individual searches.