Assessing wind impact on semi-autonomous drone landings for in-contact power-line inspection

Author:

Gendron Étienne1ORCID,Leclerc Marc-Antoine1ORCID,Hovington Samuel1ORCID,Perron Étienne1,Rancourt David1ORCID,Lussier-Desbiens Alexis1ORCID,Hamelin Philippe1,Girard Alexandre1ORCID

Affiliation:

1. Mechanical departement, University of Sherbrooke, Sherbrooke, Quebec, Canada

Abstract

In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The system’s performance is assessed by testing two landing strategies, adjusting controller parameters, and considering four different levels of wind intensity. The results show that a two-stage landing strategy offers higher probabilities of landing success and gives insight regarding the best controller parameters and the maximum wind level for which the system is robust. Finally, an experimental demonstration of the system landing autonomously on a power line is presented.

Funder

Consortium de Recherche et d'innovation en Aérospatiale au Québec

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

Canadian Science Publishing

Subject

Control and Optimization,Electrical and Electronic Engineering,Control and Systems Engineering,Automotive Engineering,Aerospace Engineering,Computer Science Applications

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3