Global Path Planning Of Unmanned Surface Vessel Based On Multi-objective Hybrid Particle Swarm Algorithm

In recent years,the application of unmanned surface vessel(USV)in the national defense and civil field is increasing,and has become one of the hot research at home and abroad.However,the research of USV in China started late,so it is basically in the remote control stage at present,and there is a big gap with foreign countries.Therefore,China is actively carrying out the study of USV technology.Autonomous path planning is one of the core functions of USV,which represents the intelligent level of unmanned ships,and includes three aspects: global path planning,local path planning and formation path planning.Among them,the global path planning is not only the prerequisite for USV's safe navigation,but also the basis of local path planning,so it has considerable research value.However,at present,the global path planning algorithms used by scholars are easy to fall into the local optimal solution and has the slow convergence rate.Moreover,most scholars consider the shortest path as the single target,resulting in the planned path with large corners or too close to obstacles.In order to solve these problems,the global path planning of USV is studied in this paper based on the 100-ton intelligent USV project,and the purpose is to design an efficient global path planning algorithm which meets the actual navigation requirements of USVs.This paper focuses on the research and design of the global path planning of USVs.The main work is as follows:Firstly,on the basis of a lot of research,this paper analyzes the research status and main gap of USVs at home and abroad.Then it analyzes the research status and research trend of path planning technology.Subsequently,with the actual project demand,the overall architecture and implementation flow of the autonomous route planning of USV are designed.Secondly,on the basis of introducing and analyzing the global path planning algorithm,this paper firstly constructs the environment model on the MATLAB platform by using the MAKLINK method,and then uses the Dijkstra algorithm to search the link diagram of the environment model;Finally,a hybrid particle swarm optimization algorithm based on simulated annealing is used to optimize the initial path and search for a global shortest path.The hybrid particle swarm optimization algorithm and the ant colony algorithm are simulated and analyzed by MATLAB platform,and the feasibility and superiority of the hybrid particle swarm algorithm used in this paper are verified.Finally,in order to improve the practicability of the algorithm,this paper improves the hybrid particle swarm optimization algorithm to achieve multi-objective optimization,which includes the path length,path smoothness and path security.And the multi-objective optimization simulation of hybrid particle swarm optimization(PSO)is carried out by MATLAB platform.Therefore,the feasibility and advancement of hybrid particle swarm algorithm based on multi-objective optimization are verified by comparison with single-objective hybrid particle swarm optimization algorithm and ant colony algorithm.In addition,this paper also presents a path planning performance evaluation method,which is used to evaluate the path planning algorithm.The evaluation value can be used as the basis for judging the merits of the algorithm.