考虑异巢起降的无人机山地巡检覆盖路径规划

doi:10.12305/j.issn.1001-506X.2025.08.19

摘要/Abstract

摘要：

为解决山地区域的防火巡检问题，提出基于异巢起降运行模式的多无人机覆盖路径规划模型。首先，提出山地区域的空中覆盖视觉航路点生成方法，建立风影响下的无人机能量消耗模型，其次，提出异巢起降的运行模式，以总能耗最小、总转弯角最小和单无人机最大能耗最小作为优化目标，建立多无人机覆盖路径规划模型，最后，提出两阶段求解算法，一阶段将航路点的遍历问题建模为旅行商问题生成总能耗与总转弯角较小的回路，二阶段计算机巢的最优插入位置以实现单无人机最大能耗最小化。对比实验表明，所提出的算法在3个目标上相比于牛耕法路径均取得一定提升。所提算法能够兼顾总能耗、总转弯角与单无人机最大能耗规划多无人机覆盖路径。

关键词: 多无人机, 覆盖路径规划, 三维路径规划, 异巢起降, 多目标优化

Abstract:

To address the issue of efficient patrol in mountainous areas, a multi-unmanned aerial vehicle （UAV） coverage path planning model based on different takeoff and landing nests operation mode is proposed. Firstly, the method of generating the aerial coverage visual route points in the mountain area is proposed, and the UAV energy consumption model under the influence of wind is established. Secondly, different takeoff and landing nests operation mode is proposed, with the overall minimum, total turn angle minimum and single drone maximum energy consumption minimum as the optimization target, multi-UAV coverage path planning model is established. Finally, a two stage solution algorithm is proposed, the first stage waypoint traversal problem modeling for traveler salesman problem problem calculation total energy consumption and total turn angle smaller loop, the second stage computes nest the optimal insertion position to deal with single UAV maximum energy consumption minimum target. The comparative experiment show that the proposed algorithm has achieved some improvement compared with the Boustrophedon method in the three targets.The proposed algorithm can balance total energy consumption, total turning angle, and maximum energy consumption of a single UAV in planning multiple drone coverage paths.

Key words: multi-unmanned aerial vehicle （UAV）, coverage path planning, three-dimensional path planning, different takeoff and landing nests, multi objective optimization

中图分类号:

V 279

羊钊, 胡锦标, 王艳, 齐洪彪. 考虑异巢起降的无人机山地巡检覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(8): 2622-2631.

Zhao YANG, Jinbiao HU, Yan WANG, Hongbiao QI. UAV coverage path planning for mountain patrol considering different takeoff and landing nests[J]. Systems Engineering and Electronics, 2025, 47(8): 2622-2631.

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参数	符号	取值
水平分辨率	r_h	4 000
垂直分辨率	r_v	3 000
对角视场角/（°）	α	84
重量/kg	m	3.78
桨叶长度/m	R	0.20
电池能量/Wh	E_M	131.6
最大飞行时间/min	t_M	41
测试速度/(m/s)	v_test	10

参数	取值	参数	取值
l_f /m	1	p_min	32
o	0.2	g/(m/s²)	9.8
k₁	0.855 4	c₂	0.317 7
v_v /(m/s)	3	v_w /(m/s)	−3
v_w /(m/s)	2	d_w /(°)	135
λ_E	0.7	—	—

算法	参数	取值
LKH	运行次数	3
NSGA-II	种群中个体数	50
NSGA-II	最大迭代次数	1 000
遗传算法	变异概率	0.5
	种群中个体数	100
	最大迭代次数	100

参数	牛耕法	同巢起降	异巢起降
总体能耗/kJ	1 168.87	1 156.73	1 111.34
改善率/%	—	1.04	4.75
单无人机最高能耗/kJ	302.84	301.23	293.74
改善率/%	—	0.53	3.01
总转弯角/(°)	8 825.08	22 983.88	7 498.66
改善率/%	—	−160.44	15.03

参数	牛耕法	同巢起降	异巢起降
总体能耗/kJ	1 177.19	1 141.43	1 122.35
改善率/%	—	3.04	4.66
单无人机最高能耗/kJ	302.80	290.87	283.86
改善率/%	—	3.94	6.25
总转弯角/(°)	11 554.09	22 692.66	7 471.17
改善率/%	—	−96.40	35.34