Research On Path Planning And Obstacle Avoidance Of Unmanned Vehicle

With the development of economy and technology,private cars have gradually become popular,various types of vehicles have been increasing,which has brought a lot of pressure to people’s travel.Traffic congestion has seriously affected people’s efficiency and quality of life.This is undoubtedly a great safety hazard for people’s work and life.With the advent of 5G,unmanned vehicles were born.It can solve this problem greatly.The most critical technology for that is path planning.There are two types of path planning: global and local.The planning of the car’s surrounding environment unchanging is the global planning;the planning when the surrounding environment is changing is the local planning.The main researches on path planning are as follows:Firstly,to conduct in-depth research on A~* algorithm,and find that the planned path has shortcomings of low search efficiency and long path length.So an improved A~*algorithm is proposed to improve the efficiency of path search and shorten the length of the planned path.Establishing a grid map and use sensor to obtain surrounding environmental information,A best path which is connecting the starting point and the target point.Finally,the simulation result shows the improved A~* algorithm is useful.Secondly,to study the artificial potential field found that it has the shortcomings of unreachable target and local minimum.The problem of unreachable target is solved by modifying the function,and the simulated annealing method is used to escape from the local minimum.The simulation result of Matlab2016 a verifies that the improved artificial potential field can solve the problem.In order to ensure the car work in the road,the road boundary repulsion potential field is increased to build the road boundary repulsion,and kinematic constraints are established to make the planned path more reasonable.Thirdly,to build a car model and use matlab to simulate the artificial potential field that increases the road boundary repulsion after solving the target unreachable and local minimum.The results show that the newly improved artificial potential field is effective.Figure 44;Table 7;Reference 64...