基于改进鸽群层级的无人机集群视觉巡检模型

doi:10.16182/j.issn1004731x.joss.21-1121

摘要/Abstract

摘要：

为解决无人机在执行输电线路巡检工作过程中易受环境干扰，以及传统单人单机工作模式巡检效率低下等问题，提出基于改进鸽群层级算法的无人机集群视觉巡检模型。根据载机车辆与待检塔位的GPS坐标计算出启航无人机的初始地标点，并规划运动轨迹；根据当前在巡无人机结束巡检点的位置更新待起飞无人机初始地标，实现启航与在巡无人机巡检地标的动态衔接，完成对地图指南针算子的改进；利用改进的自适应模板匹配算法优化在巡无人机的Adaboost视觉识别系统，通过自适应比对线路间距实现无人机与输电线路间相对位置的自主调整，在动态调节姿态的基础上提高检测质量。实验结果表明：相比于传统鸽群方法，该模型在巡检效率方面的在空巡检时间提高了12%、巡检距离提高了27.5%，风吹、地形变化的情况下巡线质量相比于常规识别模型分别提高了21%和15%。

关键词: 无人机输电线路巡检, 改进鸽群层级算法, 改进Adaboost, 视觉识别

Abstract:

Aim at UAV being vulnerable to the environmental interference and the low efficiency of the traditional single-person-UAV model in the transmission line inspection, a visual inspection model for the power line by UAV is proposed based on the improved pigeon flock hierarchy. The initial landmark point of the UAV is generated based on GPS coordinates of the aircraft-carrying vehicle and the tower to be inspected, and the movement trajectory is planned. The return point of the UAV is used to update the initial landmark of onward UAV, which realizes the dynamic handover between the work-exchanging UAV, and the landmark point is optimized. An advanced adaptive template matching algorithm is adopted to improve the adaboost visual recognition of the patrol UAV, which can autonomously adjust the relative position between UAV and the line by adaptively comparing the line spacing. Experimental results show that the proposed model improves the inspection time in the air by 12% and the distance by 27.5% in terms of inspection efficiency, and the quality of the inspection under the conditions of wind blows and terrain changes increase by 21% and 15% respectively.

Key words: UAV transmission line inspection, improved pigeon hierarchy algorithm, improved Adaboost, visual recognition

中图分类号:

TP391.9

陈麒, 崔昊杨. 基于改进鸽群层级的无人机集群视觉巡检模型[J]. 系统仿真学报, 2022, 34(6): 1275-1285.

Qi Chen, Haoyang Cui. Visual inspection model of UAV cluster based on improved pigeon flock hierarchy[J]. Journal of System Simulation, 2022, 34(6): 1275-1285.

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算法	无风/有风
算法	线路巡检距离/km	线路识别率/%
本文	5.6/4.7	100/96
A	5.9/4.8	85/72.4
SURF	6.6/5.4	79/55
SVM	6.2/5.1	96/80
V	4.9/3.9	100/97

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