Research On Unmanned Surface Vehicle Path Planning Based On Improved Potential Field Ant Colony Algorithm

With the emergence of unmanned technology and the continuous development of big data and artificial intelligence,the degree of automation of ships is getting higher and higher.China attaches great importance to and vigorously supports the construction and development of smart ships,and has issued relevant documents such as the Intelligent Ship Code and Intelligent Ship Development Action Plan(2019-2021),and established unmanned ship test sites.Many units or departments have jointly developed unmanned ships,and have achieved great achievements,which pushing China's intelligent ship development into a new era.In this paper,the grid method was used to environment modeling,and the artificial potential field method and ant colony algorithm are used to path planning of the unmanned surface vehicle.In order to solve the problems of slow convergence of traditional potential field ant colony algorithm,easy to fall into local optimum,goal unreachable or goal sway,this paper made three improvements to potential field ant colony algorithm.It solves the problem of slow convergence of the algorithm by improving the pheromone update strategy.It overcomes the shortcomings that the algorithm is easy to fall into the local optimal and the goal swings.It solves the problem of goal unreachable by improving the potential field function.The simulation of three different grid resolutions and the simulation before and after the algorithm improvements have proved the feasibility and effectiveness of the improved potential ant colony algorithm.