Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm

Abstract

With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.