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
Abstract
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.