| 钟艺,冯浩,殷晨波,王军,颜士宽,孙琳.面向起重机械巡检的无人机自主路径规划[J].电子测量与仪器学报,2026,40(3):36-45 |
| 面向起重机械巡检的无人机自主路径规划 |
| UAV autonomous path planning for patrol inspection of hoisting machinery |
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| DOI： |
| 中文关键词: 起重机械 路径规划 蜣螂优化算法 无人机 巡检 |
| 英文关键词:hoisting machinery path planning dung beetle optimization algorithm unmanned aerial vehicles patrol inspection |
| 基金项目:中国博士后科学基金(2025T180363)、江苏省自然科学基金(BK20221342)、国家自然科学基金(52105064)项目资助 |
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| Author | Institution |
| Zhong Yi | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Feng Hao | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Yin Chenbo | School of
Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China |
| Wang Jun | Jiangsu Tianzhou Testing Technology
Co., Ltd., Nanjing 210035, China |
| Yan Shikuan | Jiangsu Tianzhou Testing Technology
Co., Ltd., Nanjing 210035, China |
| Sun Lin | Wuxi Cosmo Suspended Platform Co., Ltd., Wuxi 214133,China |
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| 中文摘要: |
| 针对传统路径规划方法存在的路径冗余、易陷入局部最优等问题，提出一种改进蜣螂优化算法(improved dung beetle optimization, IDBO)，以实现对建筑起重机械无人机巡检飞行路径的高效规划。首先，引入基于佳点集的种群初始化策略，提升初始解的空间均匀性与覆盖率。其次，采用指数衰减公式动态调节扰动因子，实现全局探索与局部开发能力的自适应平衡。最后，引入柯西-高斯混合变异机制，对停滞种群实施变异操作以抑制算法早熟收敛，增强全局搜索性能。在基准测试集上的实验表明，IDBO在收敛速度与求解精度上均优于对比算法，综合排名第1，验证了改进策略的有效性。进一步地，通过构建融合路径长度、能源损耗与威胁成本的综合评价模型，基于实际工地场景建立包含多个建筑起重机械的三维地图并开展实验。结果表明，IDBO规划的巡检路径不仅可以有效规避障碍物，并且在3种复杂度各不相同的场景中，其目标函数值较基准方法分别提升11.47%、7.23%与9.17%。在多障碍物，多巡检目标和多元成本并存的复杂工地环境下，所提方法可为无人机路径规划提供有效解决方案，具有较高的应用前景。 |
| 英文摘要: |
| To address the issues of path redundancy and susceptibility to local optima in traditional path planning methods, this paper proposes an improved dung beetle optimization (IDBO) algorithm for efficient flight path planning of unmanned aerial vehicle (UAV) inspection of construction hoisting machinery. The core improvements include three mechanisms: A population initialization strategy based on a good point set to enhance spatial uniformity and coverage of initial solutions; an exponential decay formula to dynamically adjust the perturbation factor for adaptive balance between global exploration and local exploitation; and a hybrid Cauchy-Gaussian mutation mechanism to mutate stagnant populations, thereby inhibiting premature convergence and enhancing global search performance. Experimental results on benchmark test sets demonstrate that the proposed IDBO algorithm outperforms comparable algorithms in both convergence speed and solution accuracy, securing the top comprehensive ranking. For the UAV inspection application, a comprehensive evaluation model was formulated, integrating critical factors including path length, energy consumption, and threat cost. Simulations conducted within a realistic 3D construction site model populated with multiple hoisting machinery confirm that the paths planned by IDBO not only effectively avoid obstacles but also yield significant improvements in the objective function value. Specifically, in three scenarios of varying complexity, the performance improved by 11.47%, 7.23%, and 9.17%, respectively, when compared to the baseline method. Consequently, the proposed IDBO algorithm provides an effective and robust solution for autonomous UAV path planning in complex construction environments characterized by multiple obstacles, numerous inspection targets, and multi-dimensional costs, demonstrating considerable application potential. |
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