Research on Global Path Planning of Unmanned Ground Vehicles in Off-Road Environments Based on Improved A* Algorithm
Jiading Bao, Lei Zhang, Cong Li, Hui Jing, Huanqin Feng, Lanwen Wang · 2024
In off-road environments, where road information is unclear, terrain is very rugged, and the boundaries between passable and impassable areas are indistinct, paths planned by traditional algorithms tend to result in low lateral driving stability and high energy consumption. To address these problems, this research proposes improvements to the environmental modeling approach and cost function of the traditional $\mathrm{A}^{*}$ algorithm, aiming to generate paths that consider distance, energy consumption, and lateral driving stability. Firstly, slope calculations are applied to the initial Digital Elevation Model (DEM) data to create a vehicle-passable map based on the vehicle’s maximum pitch angle, maximum rollover angle, and manually designated obstacle regions. Secondly, vehicle pitch and rollover angle factors are incorporated into the cost function of the traditional $\mathrm{A}^{*}$ algorithm. Finally, the simulation results on maps of different sizes show that compared with the traditional A* algorithm, the path planned by the algorithm in this research is able to reduce energy consumption and improve the stability of the vehicle’s lateral travel in off-road environments, with less increase in the path distance.