Study on robust aerial docking mechanism with deep learning based drogue detection and docking

Cited 14 time in webofscience Cited 5 time in scopus
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dc.contributor.authorChoi, Andrew Jaeyongko
dc.contributor.authorYang, Hyeon-Hoko
dc.contributor.authorHan, Jae-Hungko
dc.date.accessioned2021-03-11T01:50:07Z-
dc.date.available2021-03-11T01:50:07Z-
dc.date.created2021-01-26-
dc.date.issued2021-06-
dc.identifier.citationMECHANICAL SYSTEMS AND SIGNAL PROCESSING, v.154-
dc.identifier.issn0888-3270-
dc.identifier.urihttp://hdl.handle.net/10203/281445-
dc.description.abstractThis paper proposes a simple and a robust bistable docking system with a deep learning based real-time drogue detection and tracking system for Unmanned Aircraft Systems (UAS) for mid-air autonomous aerial docking. Secure aerial docking mechanisms between the leader and follower aerial vehicles with effective drogue detection and tracking strategies are fundamental challenges during the air-to-air docking phase of autonomous aerial docking. To confront those issues, this paper not only presents the design of a bistable-based aerial docking mechanism, but also proposes effective deep learning based real-time drogue detection using a convolutional neural network (CNN) and real-time tracking algorithm using a point cloud algorithm. To ensure novelty and robustness for the aerial docking mechanism, a foldable bistable gripper-type mechanism is designed to increase the grasping performance with simplicity and adaptability. The proposed gripper acts as a drogue by itself to grasp a probe which is attached to the follower aerial vehicle. To employ an effective drogue detection method, the deep learning based real-time object detection algorithm, YOLOv3, is used to implement the drogue detection system. The proposed new probe-and-drogue type bistable docking system has the advantages of being simple and robust. The deep learning based real-time drogue detection method increases the detection rate. Moreover, the real-time tracking algorithm with a depth camera system does not require a GPS/INS system and many other sensors to follow the drogue movement in the air. (C) 2021 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.publisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD-
dc.titleStudy on robust aerial docking mechanism with deep learning based drogue detection and docking-
dc.typeArticle-
dc.identifier.wosid000615989700009-
dc.identifier.scopusid2-s2.0-85099043910-
dc.type.rimsART-
dc.citation.volume154-
dc.citation.publicationnameMECHANICAL SYSTEMS AND SIGNAL PROCESSING-
dc.identifier.doi10.1016/j.ymssp.2020.107579-
dc.contributor.localauthorHan, Jae-Hung-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAerial docking-
dc.subject.keywordAuthorProbe-and-drogue system-
dc.subject.keywordAuthorBistable mechanism-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorReal-time detection-
dc.subject.keywordAuthorYOLOv3-
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