Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning

Yanchu Li (), Xinzhou Dong, Qingqing Ding, Yinlong Xiong, Huilian Liao and Tao Wang
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Yanchu Li: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Xinzhou Dong: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Qingqing Ding: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Yinlong Xiong: Guangdong Power Grid Co., Ltd. Foshan Power Supply Bureau, Foshan 528000, China
Huilian Liao: State Grid Henan Electric Power Company DC Center, Zhengzhou 450018, China
Tao Wang: State Grid Henan Electric Power Company DC Center, Zhengzhou 450018, China

Energies, 2024, vol. 17, issue 21, 1-18

Abstract: The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection.

Keywords: UAV; power line inspection; path planning; A-Star algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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