DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Andrew Jaeyong | ko |
dc.contributor.author | Yang, Hyeon-Ho | ko |
dc.contributor.author | Han, Jae-Hung | ko |
dc.date.accessioned | 2021-03-11T01:50:07Z | - |
dc.date.available | 2021-03-11T01:50:07Z | - |
dc.date.created | 2021-01-26 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.citation | MECHANICAL SYSTEMS AND SIGNAL PROCESSING, v.154 | - |
dc.identifier.issn | 0888-3270 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281445 | - |
dc.description.abstract | This 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.language | English | - |
dc.publisher | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD | - |
dc.title | Study on robust aerial docking mechanism with deep learning based drogue detection and docking | - |
dc.type | Article | - |
dc.identifier.wosid | 000615989700009 | - |
dc.identifier.scopusid | 2-s2.0-85099043910 | - |
dc.type.rims | ART | - |
dc.citation.volume | 154 | - |
dc.citation.publicationname | MECHANICAL SYSTEMS AND SIGNAL PROCESSING | - |
dc.identifier.doi | 10.1016/j.ymssp.2020.107579 | - |
dc.contributor.localauthor | Han, Jae-Hung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Aerial docking | - |
dc.subject.keywordAuthor | Probe-and-drogue system | - |
dc.subject.keywordAuthor | Bistable mechanism | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Real-time detection | - |
dc.subject.keywordAuthor | YOLOv3 | - |
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