Research On Collision Avoidance Path Planning Method Of Unmanned Surface Vehicle

As the application of unmanned boats on the sea is more and more extensive,how to ensure the safe navigation of unmanned boats has become a concern.In the face of various complex and diverse navigation tasks at sea,unmanned boats need to avoid collisions with other ships or obstacles to ensure the safety of ships and maximize the advantages of unmanned boats in avoiding obstacles.Therefore,the research on collision avoidance path planning method of USV is particularly important.The purpose of this thesis is to explore the collision avoidance trajectory planning of USV in encounter situation,including the research on improving the local collision avoidance path planning of artificial potential field and the global collision avoidance path planning of quantum particle swarm optimization algorithm.Combined with the existing research results at home and abroad,the collision avoidance decision rules are followed to improve the collision avoidance ability and navigation efficiency of USV.The specific research contents are as follows :(1)The kinematics model and interference model of USV are analyzed.The relevant reference coordinate system of USV motion and the required kinematics and dynamics model are established.The six degree of freedom(6-DOF)mathematical model of USV is constructed.According to the motion characteristics of USV,the model is simplified accordingly,and the three degree of freedom(3-DOF)mathematical model is deduced.The calculation of the code of conduct,motion parameters and collision risk based on the maritime collision avoidance rules is further constructed,and the collision avoidance strategy is proposed to determine the avoidance measures to be taken,so as to facilitate the subsequent collision avoidance path planning in different encounter situations.(2)In view of the shortcomings and problems of the traditional artificial potential field algorithm in the collision avoidance path planning of unmanned surface vehicles,it is easy to fall into the local minimum value and difficult to get rid of the target point.It is usually only applicable to the static environment and only considers the obstacles and target points in the environment.It is difficult to deal with obstacles in the dynamic environment and does not consider the interaction with other ships,and cannot meet the actual planned collision avoidance path of unmanned surface vehicles.The gravitational potential field and repulsive potential field functions of the improved artificial potential field are proposed,and the collision avoidance rules under the COLREGS constraint are combined to enable the USV to perform regular collision avoidance when encountering obstacles,thus ensuring the safe navigation of the USV at sea.In the MATLAB simulation environment,the collision avoidance experiments were carried out on the classic encounter situations such as USV and target obstacle ship overtaking,encountering,left and right crossing.Ensure that the USV avoids the target obstacle ship safely according to the established collision avoidance rules,and avoids drift and non-compliance in the traditional algorithm.(3)Aiming at the problem that the traditional particle swarm optimization algorithm has a slow convergence speed and is easy to fall into local extreme points,the global optimal solution cannot be found,which leads to the performance degradation of the USV collision avoidance algorithm based on particle swarm optimization.A multi-USV collision avoidance planning algorithm based on improved quantum particle swarm optimization is proposed.The particles of quantum bound state are obtained from the perspective of quantum mechanics.The mapping relationship between the collision avoidance problem of USV and the particles of QPSO algorithm is constructed by introducing diversity and selection strategy.The selection operation is applied at the global best position to enrich the diversity of the population.By constructing the multi-objective optimization collision avoidance constraints of USV,the limitations of the standard particle swarm optimization algorithm are overcome,and the global search ability of the optimization algorithm is improved.The simulation results show that the algorithm is superior to the USV collision avoidance path method based on traditional particle swarm optimization in terms of convergence accuracy and iteration times.At the same time,minimize the risk of collision avoidance.