数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (3): 185-193.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.03.015

doi: 10.11871/jfdc.issn.2096-742X.2025.03.015

• 技术与应用 • 上一篇    下一篇

固定翼无人机异地起降大范围自动巡检技术设计

李宇程1,*(),黄志都2,杨钦3   

  1. 1.广西电网有限责任公司，广西 南宁 530000
    2.广西电网有限责任公司电力科学研究院，广西 南宁 530023
    3.广西电网有限责任公司崇左供电局，广西 崇左 532200
  • 收稿日期:2024-10-21 出版日期:2025-06-20 发布日期:2025-06-25
  • 通讯作者: *李宇程（E-mail: pire234@126.com）
  • 作者简介:李宇程，广西上林县人，大学本科，工程师，研究方向：电网设备无人机智能巡视技术应用。
    负责论文初稿撰写，包括整体思路构建、技术内容阐述、实验设计与结果分析等以及论文修订。
    LI Yucheng, with a bachelor’s degree, is an engineer from Shanglin County, Guangxi. His current research interests focus on the application of UAV-based intelligent inspection technology for power grid equipment.
    In this paper, he is responsible for drafting the initial manuscript, including the overall research framework, technicalelaboration, experimental design, result analysis, and paper revision.
    E-mail：pile234@126.com

Design of a Large-scale Automatic Inspection Technology for Fixed Wing Unmanned Aerial Vehicles Taking off and Landing in Different Locations

LI Yucheng1,*(),HUANG Zhidu2,YANG Qin3   

  1. 1. Guangxi Power Grid Co. Ltd, Nanning, Guangxi 530000, China
    2. Electric Power Research Institute of Guangxi Power Grid Co. Ltd., Nanning, Guangxi 530023, China
    3. Chongzuo Power Supply Bureau of Guangxi Power Grid Co. Ltd, Chongzuo, Guangxi 532200, China
  • Received:2024-10-21 Online:2025-06-20 Published:2025-06-25

RichHTML

1

PDF

4

摘要：

【目的】为了解决固定翼无人机大范围巡检时起降场地、巡检范围及自动巡检能力方面的关键问题，提升综合性能。【方法】该研究基于CortexA53架构的巡检硬件系统在嵌入式Debian操作系统的基础上，结合傅里叶变换的梯度方向直方图和自抗扰算法，对固定翼无人机异地起降和巡检图像信息进行处理。采用自抗扰控制器，提高飞机在起降过程的抗风扰能力。本研究采用基于目标特性的小目标检测手段，对图像进行目标检索。【结果】通过试验，作业效率和覆盖范围显著提升11.9%，该技术方案为复杂环境大范围巡检提供可靠高效解法，有力推动无人机巡检技术在多领域广泛应用。

关键词: 固定翼无人机, 异地起降, 目标特征提取, 自动巡检, 傅里叶变换, 自抗扰算法, 闭环控制

Abstract:

[Objective] This study aims to address key issues related to the takeoff and landing site, inspection range, and automatic inspection capability of fixed wing unmanned aerial vehicles during large-scale inspections, with the goal of improving overall performance. [Methods] This study is based on the CortexA53 architecture of the inspection hardware system, which is embedded in the Debian operating system. It combines Fourier transform gradient direction histogram and a self-disturbance rejection algorithm to process fixed wing unmanned aerial vehicle takeoff and landing and inspection of image information. An active disturbance rejection controller is employed to enhance the aircraft’s wind resistance during takeoff and landing. This study adopts a small object detection method based on target characteristics to perform object retrieval on images. [Results] Through experiments, the efficiency and coverage of the task have significantly increased by 11.9%. This technical approach offers a reliable and efficient solution for large-scale inspections in complex environments, effectively promoting the widespread application of drone inspection technology in multiple fields.

Key words: fixed wing drones, off site takeoff and landing, target feature extraction, automatic inspection, Fourier transform, self disturbance rejection algorithm, closed-loop control