Research On Path Planning Technology For Automatic Driving System

Autonomous driving is a hot frontier technology in the field of science and technology at present.Spurred by Google,tesla,Uber and other technology companies,scientific research institutions and automobile manufacturers have deployed resources to accelerate the research and development related to this technology.Path planning technology is an essential technology module in the automatic driving system.Path planning relies on high-precision map to plan the optimal route for the vehicle,so as to achieve the task requirements from the starting point to the target point.Path planning technology is also widely used in game route search,sweeping robot,logistics distribution,warehouse inspection and other scenarios.Therefore,path planning algorithm is required to be able to adapt to more complex environment map and save time cost.Starting from the characteristics of the algorithm itself and the complexity of the environment,it is of great significance to study the path planning technology by improving the single algorithm and integrating multiple algorithms.This paper studies the algorithms of global path planning and local path planning respectively.According to the principle of global path planning A* algorithm,the simulation compares the path planning effect of four different heuristic functions commonly used in A* algorithm,and puts forward an improved heuristic function with weighted Manhattan distance.The simulation verifies that the heuristic function improves the path planning efficiency of A* algorithm,and has advantages in the length of the searched path,the search time and the number of nodes searched.According to the principle of local path planning DWA algorithm,how to select the weight of three evaluation indexes of the evaluation function of this algorithm is simulated and analyzed.By fusing A* algorithm with weighted Manhattan distance and DWA algorithm,the fusing algorithm is made as close as possible to the global optimal path,which effectively avoids the shortcoming that DWA algorithm is prone to fall into local optimal,and meanwhile reduces the running time of the algorithm.According to the key technology composition of the automatic driving system,the robot chassis and lidar are selected,and the hardware platform of the unmanned vehicle is built;Based on the working framework and basic characteristics of ROS system,the function of each module of navigation stack organization framework was analyzed in detail,and the specific method of move_base package for path planning algorithm verification was studied.The algorithm plug-in and robot visualization model studied in this paper were configured.In the experimental part,the key parameters in move_base package were configured,the static and dynamic experimental environments were set up,and the experimental verification and result analysis of path planning were completed,which proves that the algorithm studied in this paper has certain practicability.