电子测量与仪器学报

寇志伟,景高乐.基于地形因子的草原风场巡检机器人路径规划研究[J].电子测量与仪器学报,2025,39(9):99-110

基于地形因子的草原风场巡检机器人路径规划研究

Study on path planning algorithm for inspection robots in grassland windpower station based on terrain factors

DOI：

英文关键词:inspection robots grassland wind power station path planning terrain factors TF-A* algorithm

基金项目:国家自然科学基金（62241309）、内蒙古自治区自然科学基金（2024LHMS06023）、内蒙古科技计划项目 (2021GG0256)资助

作者	单位
寇志伟	1.内蒙古工业大学电力学院呼和浩特010080；2.大规模储能技术教育部工程研究中心呼和浩特010080
景高乐	内蒙古工业大学电力学院呼和浩特010080

Author	Institution
Kou Zhiwei	1.School of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; 2.Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot 010080, China
Jing Gaole	School of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China

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中文摘要:

草原风场具有风力等级较大、地表高低起伏、地面局部坑洼等特点，地面巡检机器人在不同风力等级状态下执行巡检任务时路径指标与安全性能无法兼顾，对路径规划方法提出了更高的要求。提出了一种基于地形因子的改进A*路径规划算法（terrain factors A*, TF-A*）。首先，基于草原风场的高低起伏地形与局部坑洼地形设计了坡度因子、阶跃因子，优化了代价函数与启发函数，构建了TF-A*路径规划算法。其次，考虑到草原风场环境风力等级较大，优化了不同风力等级状态时坡度因子参数与阶跃因子参数阈值，有效的提高了巡检机器人的安全性能。然后分别开展了TF-A*路径规划算法的短距离仿真测试、长距离仿真测试与实地实验测试。实验结果表明，TF-A*路径规划方法较传统A*算法在两种典型风力状态下路径长度指标分别提升了44.55%、34.82%，搜索时间指标分别提升了22.06%、23.16%，在风力等级较小时优先考虑距离指标，在风力等级较大时兼顾了距离指标与运行安全性。为草原风场非结构化不平坦地形场景的机器人巡检与路径规划提供了新的思路。

英文摘要:

The grassland wind power station is characterized by high wind force levels, undulating terrain, and uneven ground surfaces. When ground inspection robots perform patrol tasks under different wind force conditions, it is challenging to balance path indicators with safety, thus posing higher demands on path planning methods. This paper proposed an A* path planning algorithm enhanced with terrain factors（A* algorithm (TF-A*）. Initially, this paper designed a gradient factor and a step factor based on the undulating terrain with high and low bumps and the terrain with pits and steep slopes of the grassland wind power station, and applied these factors to optimize the cost function and the heuristic function. Building upon these enhancements, the paper successfully developed an A* path planning algorithm enhanced with terrain factors. Subsequently, this paper meticulously tailored the parameters for both the slope factor and the step factor to accommodate varying wind force conditions. By taking into account the significant wind force levels prevalent in the grassland wind power station, which has substantially improved the safety and stability of the inspection robots. Following that, a series of experimental evaluations were meticulously executed, including short-distance and long-distance simulation tests, as well as real-world field experiments, to assess the efficacy of TF-A* path planning algorithm. The experimental outcomes have revealed that the TF-A* path planning method has significantly surpassed the traditional A* algorithm, with a notable 44.55% and 34.82% increase in path length metrics, and a substantial 22.06% and 2316% reduction in search time metrics across two distinct weather conditions. Specifically, under conditions of low wind force, the method strategically prioritizes distance metrics, where-as under high wind force, it adeptly integrates both distance metrics and operational safety into its considerations. It provides a novel approach for robot inspection and path planning in unstructured and uneven terrains of grassland wind power station.

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