Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring

This paper proposes a framework for obstacle-avoiding autonomous unmanned aerial vehicle (UAV) systems with a new obstacle avoidance method (OAM) and localization method for autonomous UAVs for structural health monitoring (SHM) in GPS-denied areas. There are high possibilities of obstacles in the p...

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Bibliographic Details
Published in:Structural health monitoring Vol. 23; no. 2; p. 971
Main Authors: Waqas, Ali, Kang, Dongho, Cha, Young-Jin
Format: Journal Article
Language:English
Published: England 01.03.2024
Subjects:
obstacle avoidance algorithm
deep learning
damage segmentation
Autonomous flight
GPS-denied areas
UAV localization
unmanned aerial system
ISSN:1475-9217
Online Access:Get more information
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Summary:This paper proposes a framework for obstacle-avoiding autonomous unmanned aerial vehicle (UAV) systems with a new obstacle avoidance method (OAM) and localization method for autonomous UAVs for structural health monitoring (SHM) in GPS-denied areas. There are high possibilities of obstacles in the planned trajectory of autonomous UAVs used for monitoring purposes. A traditional UAV localization method with an ultrasonic beacon is limited to the scope of the monitoring and vulnerable to both depleted battery and environmental electromagnetic fields. To overcome these critical problems, a deep learning-based OAM with the integration of You Only Look Once version 3 (YOLOv3) and a fiducial marker-based UAV localization method are proposed. These new obstacle avoidance and localization methods are integrated with a real-time damage segmentation method as an autonomous UAV system for SHM. In indoor testing and outdoor tests in a large parking structure, the proposed methods showed superior performances in obstacle avoidance and UAV localization compared to traditional approaches.
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ISSN:1475-9217
DOI:10.1177/14759217231177314