关键词: 无人履带车辆, 越野环境, 路径规划, 节能优化, 概率图算法

Abstract: Global path planning in complex off-road environments is one of the key technologies for realizing unmanned driving of ground equipment. However, there is limited research on energy-efficient path planning for tracked vehicles in the industry, and currently common algorithms struggle to balance solution quality with computational efficiency, making them impractical for real-time energy-optimal global path planning of tracked vehicles. To address this issue, this paper proposes the Optimal Energy-Fast Probabilistic Roadmap Method (OPRM) for tracked vehicles: An off-road energy consumption cost model considering the characteristics of tracked vehicles was established to quantify their energy consumption in off-road conditions; By creating specific vectors, the sampling method of the probabilistic roadmap algorithm in off-road environments was directionally improved, enhancing the algorithm's running speed while reducing the energy consumption of tracked vehicles; Meanwhile, the method prevents interference between paths and the environment by increasing path node density. Simulation experiments demonstrate that in off-road environments, compared with traditional algorithms, the proposed method can reduce energy consumption by up to 32% and decrease planning time by up to 89.2%, achieving comprehensive optimization of both travel energy consumption and algorithm runtime for global path planning of tracked vehicles under off-road conditions.

Key words: unmanned tracked vehicles, off-road environments, path planning, energy efficiency optimization, probabilistic roadmap algorithm