Study on path planning of unmanned surface vessel based on data-driven genetic algorithm - Junfeng XIN; Yongbo ZHANG; Jiageng BO; Bowen ZHAO; Shiyuan FAN (智能科学与技术学报)
The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed,which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm,the LCPGA increases the diversity of the population,avoids falling into local optimum more effectively,and improves the accuracy,robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel.