Path Planning Method for Unmanned Vehicles in Complex Off-Road Environments Based on an Improved A* Algorithm

Jinyin Bai, Wei Zhu (), Shuhong Liu, Lingxin Xu and Xiangchen Wang
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Jinyin Bai: School of Information and Communication, National University of Defense Technology, Wuhan 430034, China
Wei Zhu: School of Information and Communication, National University of Defense Technology, Wuhan 430034, China
Shuhong Liu: School of Information and Communication, National University of Defense Technology, Wuhan 430034, China
Lingxin Xu: School of Information and Communication, National University of Defense Technology, Wuhan 430034, China
Xiangchen Wang: School of Information and Communication, National University of Defense Technology, Wuhan 430034, China

Sustainability, 2025, vol. 17, issue 11, 1-19

Abstract: In recent years, autonomous driving technology has made remarkable progress in urban transportation and logistics, while its application in complex off-road environments has gradually become a research hotspot. Compared to traditional manned vehicles, unmanned vehicles demonstrate higher safety and flexibility in scenarios such as rapid transportation, emergency rescue, and environmental reconnaissance. However, current research on path planning is predominantly focused on structured environments, with limited attention given to unstructured off-road conditions. This paper proposes an improved A* algorithm tailored to address the challenges of path planning in complex off-road environments. First, a grid map incorporating multi-dimensional information is constructed by integrating elevation data, risk zones, and surface attributes, significantly enhancing environmental perception accuracy. At the algorithm level, the heuristic function and search strategy of the A* algorithm are optimized to improve its efficiency and path smoothness in complex terrains. Furthermore, the method supports the flexible planning of three types of paths—minimizing time, minimizing risk, or optimizing smoothness—based on specific task requirements. Simulation results demonstrate that the improved A* algorithm effectively adapts to dynamic off-road environments, providing intelligent and efficient path planning solutions for unmanned vehicles. The proposed method holds significant value for advancing the application of autonomous driving technology in complex environments.

Keywords: complex environments; grid map; autonomous driving; path planning; A* algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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