DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Andrew Jaeyong | ko |
dc.contributor.author | Park, Jeonghawn | ko |
dc.contributor.author | Han, Jae-Hung | ko |
dc.date.accessioned | 2021-08-24T01:10:11Z | - |
dc.date.available | 2021-08-24T01:10:11Z | - |
dc.date.created | 2021-08-23 | - |
dc.date.created | 2021-08-23 | - |
dc.date.issued | 2021-03-22 | - |
dc.identifier.citation | Active and Passive Smart Structures and Integrated Systems XV 2021 | - |
dc.identifier.uri | http://hdl.handle.net/10203/287377 | - |
dc.description.abstract | Unmanned aerial systems (UAS) with embedded machine learning applications are applied in various fields for autonomous aerial refueling (AAR), concept of parent-child UAV system, drone swarm, teaming of manned aircraft and UAV, package delivery, etc. The fundamental challenge of an air-to-air docking phase is securing between a leader and a follower aerial vehicles with effective target detection strategy. This paper proposes an autonomous docking system for unmanned aerial vehicle (UAV) system that detects, tracks, and docks to a drogue. The proposed system is operated on an onboard machine learning computer platform. This paper presents not only the design of a probe-and-drogue type of docking system based on bi-stable mechanism, but also the development of an onboard machine learning system for a simple and a robust mid-air docking. ARM-based computer, Jetson Xavier NX module, is used as a companion computer to perform a real-time detection and an autonomous control for the aerial vehicle. To employ an effective drogue detection, a deep learning convolutional neural network (CNN) based real-time object detection algorithm, YOLOv4 tiny, is applied. Furthermore, a point-cloud based tracking algorithm with a RGB-D camera system is developed to track the drogue movement in the air. Before conducting an outfield docking test, a performance of the proposed docking system is validated. | - |
dc.language | English | - |
dc.publisher | SPIE | - |
dc.title | Development of mid-air autonomous aerial docking system using onboard machine learning computations | - |
dc.type | Conference | - |
dc.identifier.wosid | 000696723900004 | - |
dc.identifier.scopusid | 2-s2.0-85107482558 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | Active and Passive Smart Structures and Integrated Systems XV 2021 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1117/12.2583374 | - |
dc.contributor.localauthor | Han, Jae-Hung | - |
dc.contributor.nonIdAuthor | Park, Jeonghawn | - |
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