Cooperative collision avoidance for unmanned surface vehicles based on improved genetic algorithm

This paper proposes a method based on the improved genetic algorithm for cooperative collision avoidance by multiple unmanned surface vehicles (hereinafter referred to as multiple USVs). In the multiple USVs collaborative task mode in a complex environment featuring obstacles, we establish models of the multiple USVs system and sensor detection; divide scenarios involving multiple USVs encounters and design corresponding collision avoidance strategies; and calculate the motion parameters and risk of collision to determine whether to take measures to avoid collisions. Following this, we choose the genetic algorithm (GA) as core algorithm to plan for collision avoidance, improve it through retention, deletion, and replacement, use the analytic hierarchy process to build a fitness, iteratively optimize the adjustment of velocity and heading, and calculate the current best path for collision avoidance for multiple USVs. Finally, we built a simulation platform for multiple USVs collision avoidance planning based on the QT software, and designed typical cases to verify the proposed method with and without communication conditions. The results show that the proposed method can be used for the safe operation of multiple USVs.