Unmanned Boad Path Planning Based On Improvedgrid Method And Artifitial Potential Field Method

As a new kind of intelligent equipment,Unmanned Surface Vehicle is widely used in the field of Marine scientific research,Marine resources development,Marine environmental protection,Marine rescue and Marine military,and has gained increasing attention from countries around the world.Path planning is the key technology to realize intelligence and autonomy of robot navigation control system and is an important part of Unmanned Surface Vehicle research.To meet the requirements of autonomous navigation in dynamic and unpredictable Marine environments,the capacity of the path planning needs to be continuously improved to ensure that the path planned by the system is efficient and reliable and can cope with various complex marine environments.This paper focuses on the path planning of Unmanned Surface Vehicle based on A Star algorithm and artificial potential field.Including research on static global path planning in a large area of sea and research on local path planning in the unknown dynamic environment.The main work of the thesis is as follows:Firstly,introduce the research background and significance of path planning and analyze the research and development trends of Unmanned Surface Vehicle and its path planning.Secondly,analyze the principles of the main methods of path planning and their advantages and disadvantages,then determine the algorithm used in this study.For static global path planning,use A Star algorithm to get the basic global path for its good static search performance,also use expansive and erosive algorithms to process environmental information to increase search efficiency.Then subdivide the grid to improve the accuracy of path planning by combining potential field method with A Star algorithm.Propose a strategy to remove redundant nodes and redundant turning points to further improve the path performance.Finally,apply a polyline smoothing method to the resulting path for optimization to get a smooth final path.For local path planning and dynamic obstacle avoidance,select artificial potential field method for its good real-time performance.Analyze its inherent flaws and its shortcomings in the dynamic environment,then add the relative position of the boat and the target point in the gravitational potential field;add the relative speed of the boat,obstacles and target point in the gravitational potential field to reconstruct the potential field.For environmental information,take methods to connect obstacles to reduce computationalcomplexity.For the local minimum problem of artificial potential field method,propose a strategy by establishing local target points and repulsive force decomposition optimization Finally,proposing an adaptive step size optimization method to improve the performance of the algorithm in dynamic environment applications.Use MATLAB as a platform for simulation to test the improved path planning method.Compare the results of global path planning and local path planning.Use a combination of global planning and local planning to simulate experiments in the context of actual sea areas.The simulation results verify the effectiveness and feasibility of the improved algorithm.