Global Path Planning of UGVs in Large-Scale Off-Road Environment Based on Improved A-star Algorithm and Quadratic Programming
Junkai Jiang, Zeyu Han, Jinhao Li, Yuning Wang, Jianqiang Wang, Shaobing Xu · 2023
Global path planning is an essential component of intelligent vehicle study. This paper designs a two-layer global path planning method based on an improved A* algorithm and quadratic programming for UGVs in a large-scale off-road environment. In the first layer, we generate a global path from the current node to the target node via an improved A* algorithm, with a grid map containing the information of non-accessible areas and uncertainty of off-road terrains as input. The second layer smooths the entire path based on quadratic programming. We adopt efficiency improvement methods in both layers, which ensure the real-time performance of the algorithm. The planner has been verified by simulation and experiments, and the results validate the practicability and real-time performance of the designed method.