基于二维码的无人机路径规划与目标检测风电机组检测代理案例研究

Branden Pinney, Shayne Duncan, Mohammad Shekaramiz, M. Masoum
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引用次数: 4

摘要

本案例研究展示了使用全自动无人机系统降低风力涡轮机外部结构检查过程中的成本、工时和安全风险的初步结果。最终目标是使用目标检测和路径规划算法,通过无人机在现场自动识别特定风力涡轮机,安全接近风力涡轮机,并捕获分析和检查所需的图像。我们在这里的案例研究作为路径规划解决方案的小规模概念验证,使用基座风扇代替风力涡轮机和Tello EDU无人机。我们的研究表明,无人机成功地自主探索感兴趣的区域,检测所需的风扇,安全接近风扇,通过扫描相关的二维码验证风扇,从多个角度捕获视频和图像,并安全飞回起点并着陆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drone Path Planning and Object Detection via QR Codes; A Surrogate Case Study for Wind Turbine Inspection
This case study shows the initial results of aiming for reducing the cost, man-hours, and safety risks involved with the external structural inspection process of wind turbines using a fully automated drone-based system. The end goal is to use object detection and path planning algorithms to automate the process of identifying a specific wind turbine in the field via a drone, safely approaching the wind turbine, and capturing the images necessary for analysis and inspection. Our case study here serves as a small-scale proof of concept for the path planning solutions using pedestal fans in the place of wind turbines and a Tello EDU drone. Our study demonstrates the success of the drone to autonomously explore the region of interest, detect the desired fan, safely approach the fan, verify the fan via scanning the associated QR code, capture video and images from multiple angles, and safely fly back to the starting point and land.
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