面向飞机表面视觉检查的无人机覆盖路径规划

doi:10.12305/j.issn.1001-506X.2025.04.17

摘要/Abstract

摘要：

为了高效规划无人机执行飞机表面视觉检查任务时的飞行路径, 提出一种基于自适应混合采样策略的覆盖路径规划算法, 通过视点生成、视点筛选和覆盖路径规划求解无人机最优检查路径。首先, 基于待检查飞机模型进行视线最优采样和自适应补充采样, 生成冗余视点集合。然后, 采用一种基于动态加权启发式的图搜索算法, 搜索并选择一组提供增量覆盖的有效视点。最后, 在原Lin-Kernighan启发式(Lin-Kernighan heuristic, LKH)算法中设计了路径碰撞检测模块, 并通过改进后的LKH算法求解无人机无碰撞检查路径。仿真实验结果表明, 所提算法在两种不同场景下规划出的无人机检查路径最大飞机表面覆盖率分别为93.44%和96.44%, 在路径长度、视点数量和算法耗费时间方面均优于其他对比算法。

关键词: 飞机表面检查, 覆盖路径规划, 自适应混合采样, 图搜索算法, 无人机

Abstract:

In order to efficiently plan the flight paths for unmanned aerial vehicle (UAV) conducting visual inspection tasks on aircraft surface, a coverage path planning algorithm is proposed by using an adaptive hybrid sampling strategy, which involves viewpoint generation, viewpoint selection, and coverage path planning to determine the optimal inspection path for UAV. Firstly, optimal line-of-sight sampling and adaptive supplementary sampling are performed based on the aircraft model to be inspected to generate a redundant set of viewpoints. Secondly, a heuristic graph search algorithm based on dynamic weighting is adopted to search and select a set of effective viewpoints to offer incremental coverage. Finally, a path collision detection module is designed in the original Lin-Kernighan heuristic (LKH) algorithm and the improved LKH algorithm is used to solve UAV collisionless inspection path.Simulation experimental results demonstrate that the proposed method achieves max aircraft surface coverage rates of 93.44% and 96.44% of the aircraft inspection paths planned in two different scenarios, which outperforms other comparative algorithms in terms of path length, number of viewpoints, and algorithmic time consumption.

Key words: aircraft surface inspection, coverage path planning, adaptive hybrid sampling, graph search algorithm, unmanned aerial vehicle (UAV)

中图分类号:

陈威, 王从庆, 曾强, 李战. 面向飞机表面视觉检查的无人机覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(4): 1206-1213.

Wei CHEN, Congqing WANG, Qiang ZENG, Zhan LI. UAV coverage path planning for aircraft surface visual inspection[J]. Systems Engineering and Electronics, 2025, 47(4): 1206-1213.

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实验场景	(L×W×H)/m	g/m	ε/(°)	α	β	ω
场景1	50×36×15	1.5	30	0.1	0.45	0.000 5
场景2	30×24×10	1.5	30	0.1	0.45	0.004

d_th/m	场景1		场景2
d_th/m	视点数量	覆盖率/%	视点数量	覆盖率/%
4.0	1 486	94.01	431	96.62
3.0	620	93.44	169	96.44
2.0	236	67.74	54	71.88

路径规划算法	场景1 最大覆盖率	场景2 最大覆盖率
本文所提算法	93.44	96.44
SSCPP	92.62	93.51
ASSCPP	90.05	92.97

实验场景	规划算法	算法耗时/s	路径长度/m	视点/个
场景1	本文算法	57.3	377.85	251
	SSCPP	79.9	1 229.14	1 017
	ASSCPP	188.2	835.25	668
场景2	本文算法	28.9	88.05	72
	SSCPP	35.5	118.05	97
	ASSCPP	57.7	150.01	132

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