Path Planning of Underground Autonomous LHD Machines Based on Improved A* Algorithm
To solve the problem of safety and efficiency of path planning for autonomous LHD machines,a path planning method for underground autonomous LHD machines based on an improved A*algorithm was proposed.By extracting the roadway skeleton,the node expansion of the A*algorithm was limited to the skeleton area,which ensured that the planned path was in the central area of the roadway.Comparative experiments and path tracking applications were conducted using real map data from mines.The results show that using Manhattan distance as the heuristic function of the algorithm performs the best,and by comparing to the traditional A*algorithm,the improved A*algorithm has a planning path closer to the center of the roadway,with an average time reduction of about 76.0％and superior safety and planning speed.In on-site applications,the average tracking deviation of the autonomous LHD machine is 0.26 m,and it can safely reach the endpoint according to the planned path.The research results can provide a reference for the construction of unmanned driving systems in underground mines.
Autonomous drivingPath planningLHD machineImproved A* algorithmHeuristic function
万方数据
