Driverless Car Based On TEB Algorithm Research On Path Planning And Obstacle Avoidance Technology

Unmanned driving,as the basis of automobile intelligence,is one of the research hotspots in recent years.Path planning and obstacle avoidance technology,as a key link in the operation of unmanned vehicles,are the cornerstones of achieving safe and smooth driving of unmanned vehicles,and are of great research significance.From map generation,global path planning to local path planning,the article elaborated the whole process of unmanned vehicle path planning and obstacle avoidance in detail.Finally,the effectiveness of the improved path planning algorithm was verified through simulation and real vehicle tests.First,the laser SLAM based on the Cartographer algorithm completes the construction of the global map.The map is the basis of path planning and obstacle avoidance.In order to better study path planning and obstacle avoidance technology,this article introduces the principles and processes of the Gmapping algorithm and the Cartographer algorithm,analyzes the advantages and disadvantages of the two algorithms in the point cloud matching part and the back-end processing part,and compares them through experiments Based on the mapping effects of the two algorithms,the global map generated by the laser SLAM based on the Cartographer algorithm is finally selected as the basis of this paper.Then,the improved A* algorithm based on the fusion jumping point strategy completes the global path planning.This paper introduces the basic principles of the classic A*algorithm,analyzes the low search efficiency of the A* algorithm due to traversing repeated nodes and the possible collision risk of the path,and proposes an improved strategy that combines the jumping point strategy and the A* algorithm.And through simulation experiments,it is verified that the improved algorithm improves the optimization speed and path safety.Secondly,the parallel TEB algorithm based on spline optimization completes the local path planning of the driverless car.This article introduces the basic principles and processes of the traditional TEB algorithm.In order to solve the driverless car’s requirements for the ride comfort of the local path,this paper proposes a trajectory optimization based on cubic spline interpolation,which solves the ride comfort problem of the traditional TEB algorithm on driverless cars.In addition,in order to solve the problem of a single TEB algorithm that is easy to fall into a local minimum when facing a complex scene,this paper proposes a parallel TEB algorithm based on a topology map,through the parallel calculation of multiple initial trajectories,to obtain the global optimal path,Effectively improve the success rate of obstacle avoidance.Finally,these two optimization ideas are applied to the pre-processing and post-processing of TEB calculation respectively,and a parallel TEB algorithm based on spline interpolation is proposed.Finally,based on the ROS platform,a related simulation environment was built on the GAZABO simulation platform.The simulation verified that the improved path planning algorithm proposed in this paper can effectively improve the smoothness of the trajectory and increase the success rate of local obstacle avoidance.Finally,relying on the Formula University racing platform,the algorithm was transplanted to the real car,further verifying the effectiveness of the improved path planning algorithm.