Research On Path Planning Of Unmanned Surface Vehicle

Unmanned surface vehicle(USV)is an intelligent marine device.Compared with manned surface vehicles,it has significant advantages and can perform various special tasks in the ocean.As a complex and comprehensive system,the USV involves many technologies,among which,the path planning technology is the basic and important one.Path planning technology is the basis for USV to achieve other complex tasks,and it is a hot research in the USV field.When navigating at sea,USV will face very complicated conditions and various obstacles.It makes high demand on the autonomous path planning capability of the USV.The thesis mainly focuses on the environmental map construction,global path planning and local path planning of the USV,supported in part by the National Key Research and Development Program of the Ministry of Science and Technology of China under Grant 2017YFC1405401,in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA13030203.The representation of the environmental map is the basic issue in path planning.Before constructing an environmental map,the thesis details the components of USV system firstly and furtherly researches the path planning subsystem.On this basis,the construction of environmental map is introduced.The methods of environmental map construction,including grid method,visibility graph and Voronoi diagram,are described in detail.The environment model used in the thesis is based on the Voronoi diagram.In terms of the global path planning,traditional algorithms have poor adaptability to the environment and do not have the re-plan capability online.The probabilistic roadmap method(PRM)eliminates the need for modeling of space and only obtains obstacle information by collision detection of sampling points in configuration space to construct a roadmap,which can be reused.Path search is carried out on this roadmap.The PRM algorithm is able to solve the problem of the path planning in the complex multi-obstacle environment and have the online re-planning capability.The algorithm features easy implementation and good versatility.Simulation experiments are designed to verify the validity of the PRM algorithm in the global path planning of USV.Results show that compared with the genetic algorithm,the PRM algorithm has better performance in the terms of the running time and generated path length.In terms of local path planning,common algorithms can not meet the real-time requirements of the high-speed USV..The rapidly-exploring random tree(RRT)algorithm does not require environment modeling,can find the feesible path very fast and has probability completeness.As long as there is a feasible path in the environment,the path can be found with the increase of the number of nodes.However,the nodes in the random tree are randomly generated.To solve this problem,the idea of attractive field and repulsion field in the artificial potential field method is introduced on the basis of the basic RRT algorithm,and the RRT algorithm is improved.The improved RRT algorithm is compared with the basic RRT algorithm,artificial potential field algorithm and fuzzy logic algorithm through simulation experiments.The results show that the improved RRT algorithm overcomes the shortcomings of the randomness of the original algorithm,speed up the algorithm’s speed and performs better in terms of the generated path length and running time.