Path Planning of Electric Power Inspection Robot Based on Optimized AHA Method

Authors

  • Yupeng Li Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China , Xingtai Technology Innovation Centre for Multi-Sensor Fusion and Intelligent IoT Electronic Product, Xingtai 054000, China
  • Litao Sun Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China
  • Longxue Cheng Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China, Xingtai Technology Innovation Centre for Multi-Sensor Fusion and Intelligent IoT Electronic Product, Xingtai 054000, China
  • Xianxia Liang Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China
  • Longfei Yue Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.4022

Keywords:

α-stable distribution, fractional order calculus, flexible t distribution, EPIR, path planning

Abstract

Since the problems of artificial hummingbird algorithm (AHA) in electric power inspection robot (EPIR) path planning, including excessive route length, extended time and inadaptability, this paper proposes an improved AHA, called as IAHA. Firstly, an α-stable distribution is applied to initialize the population and enhance its diversity. Secondly, the fractional order calculus is employed to update location mechanism of artificial hummingbird, balancing global search and local excavation capabilities. Finally, flexible t distribution is utilized to improve the location strategy of artificial hummingbird, enabling IAHA to escape local optima. When the route length, inspection time and adaptability are taken as the metrics, the proposed IAHA outperforms both particle swarm optimization algorithm (PSO) and the classical AHA, which has shorter route length and inspection time.

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Author Biographies

Yupeng Li, Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China , Xingtai Technology Innovation Centre for Multi-Sensor Fusion and Intelligent IoT Electronic Product, Xingtai 054000, China

Yupeng Li, M.E., is a lecturer at the Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology. His research focuses on mobile robotics, embedded systems, applied electronics, and IoT technologies.

Litao Sun, Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China

Litao Sun, M.E., is a lecturer at the Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology. His research focuses on intelligent control systems, pattern recognition, and AI-driven defect detection.

Longxue Cheng, Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China, Xingtai Technology Innovation Centre for Multi-Sensor Fusion and Intelligent IoT Electronic Product, Xingtai 054000, China

Longxue Cheng, M.E., is a lecturer at the Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology. Her research covers applied electronics, information engineering, mobile robot localization algorithms, path planning algorithms, multi-sensor fusion frameworks, and embedded system development.

Xianxia Liang, Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China

Xianxia Liang, M.E., is a lecturer at the Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology. Her research interests include mechatronics, industrial robotics, machine vision, and image processing.

Longfei Yue, Department of Electrical Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai 054000, China

Longfei Yue, M.E., is a lecturer at the Department of Mechanical and Electrical Technology. His work centers on control theory analysis and control-related engineering applications.

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Published

2025-05-19

How to Cite

Li, Y. ., Sun, L. ., Cheng, L. ., Liang, X. ., & Yue, L. . (2025). Path Planning of Electric Power Inspection Robot Based on Optimized AHA Method. Distributed Generation &Amp; Alternative Energy Journal, 40(02), 239–258. https://doi.org/10.13052/dgaej2156-3306.4022

Issue

2025: Vol 40 Iss 2

Section

Renewable Power & Energy Systems