Zhang Hongchao, Zhao Qiancheng, Wu Yinghao, Jiang Da, Chen Xiaole, Liang Xiaoming, Sun Yunlong
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Northern Vehicle Research Institute, Beijing 100072, China.
Sensors (Basel). 2025 Sep 3;25(17):5454. doi: 10.3390/s25175454.
This paper proposes an adaptive path-planning algorithm for unmanned ground vehicles (UGVs) in three-dimensional terrain environments. The algorithm first constructs an interference model between the UGV chassis and the three-dimensional terrain, taking into account the impact of terrain undulations on vehicle driving stability. A dynamic cost-adjustment mechanism for multi-task modes was designed, which introduces a learning model to automatically identify task types and dynamically adjust the weights of various cost factors in path planning accordingly. This paper constructs simulation environments for sparse obstacle scenes and high-density obstacle scenes, respectively, to verify the effectiveness of the path-planning results of the algorithm in different task modes. The experimental results show that the proposed method can generate smoother, safer, and more task-matched trajectory paths while ensuring path feasibility, verifying the good adaptability and robustness of this algorithm for complex unstructured environments under multi-task driving conditions.
