Research On Path Planning Algorithm Of Driverless Vehicle

Path planning is one of the key technologies in the field of driverless vehicle research.In the task space of driverless vehicles,path planning is to calculate the collision free feasible path from the start to the end according to the relevant optimization criteria(shortest time,shortest path,path smoothness).In this paper,based on the defects of the rapidly exploring random tree(RRT)algorithm commonly used in the path planning of unmanned vehicles by predecessors,we improve and optimize the algorithm,and verify the improved algorithm through simulation and sample vehicle test.In this paper,a series of improvements have been made to the RRT algorithm in local path planning,such as the winding path,too many complex nodes,and long running time,etc.1)The measurement function of the algorithm has been improved according to vehicle dynamics combined with the rotation Angle constraint.2)Divide the path into regions according to the distribution of obstacles,and propose a new path node pruning method.3)Use B-spline curves for further smoothing optimization of the trimmed path.Simulation experiments are carried out in matlab to verify the superiority and feasibility of the improved algorithm in local planning.Aiming at the problem of low sampling efficiency of bidirectional search tree algorithm,an improved bidirectional fast searching random tree algorithm based on global path planning is proposed in this paper.1)Random node generation method with double sampling points is adopted to improve sampling efficiency.2)The variable probability target bias strategy is added to make the algorithm grow towards the target point on the appropriate probability,and provide the growth direction for the random tree,so that it quickly converges towards the target.3)In order to overcome the slow exploration of free space,an adaptive step size is proposed to speed up the exploration of unknown space.Matlab simulation software is used to verify the improved algorithm and the original algorithm in different types of environment maps to verify the superiority and feasibility of the improved algorithm in global planning.The hardware facilities of the automatic driving system were assembled,the construction of the prototype vehicle was completed,and the chassis control module was debugged to ensure the normal operation of the prototype vehicle.The test site of the sample vehicle was selected,and the comparison and verification of the real effects were carried out in the two scenarios.The experimental results show that the improved algorithm has better effectiveness and superiority,and is feasible in practical planning.