Title:
Multi-UAV Trajectory Optimization and Deep Learning-Based Imagery Analysis for a UAS-Based Inventory Tracking Solution

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dc.contributor.author Choi, Youngjun
dc.contributor.author Martel, Maxime
dc.contributor.author Briceno, Simon
dc.contributor.author Mavris, Dimitri N.
dc.contributor.corporatename Georgia Institute of Technology. School of Aerospace Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory en_US
dc.date.accessioned 2019-10-23T19:31:49Z
dc.date.available 2019-10-23T19:31:49Z
dc.date.issued 2019
dc.description Copyright © 2019 by the American Institute of Aeronautics and Astronautics en_US
dc.description DOI: 10.2514/6.2019-1569 en_US
dc.description.abstract This paper presents a multi-UAV trajectory optimization and an imagery analysis technique based on Convolutional Neural Networks (CNN) for an inventory tracking solution using a UAS platform in a large warehouse or manufacturing environment. The current inventory tracking method is a manual and time-consuming process to scan all the inventory items. Its accuracy is not consistent depending on the complexity of the scanning environment. To improve the scanning efficiency with respect to time and accuracy, this paper discusses a UAS-based inventory solution. In particular, this paper addresses two primary topics: multi-UAV trajectory optimization to scan inventory items and a multi-layer CNN architecture to identify a tag attached on the inventory item. To demonstrate the proposed multi-UAV trajectory optimization framework, numerical simulations are conducted in a representative inventory space. The proposed CNN-based imagery analysis framework is demonstrated on a flight experiment. en_US
dc.identifier.citation Choi, Y., Martel, M., Briceno, S., & Mavris, D.N. (2019). Multi-UAV Trajectory Optimization and Deep Learning-Based Imagery Analysis for a UAS-based Inventory Tracking Solution. The American Institute of Aeronautics and Astronautics (AIAA) Scitech, USA, 2019. 10.2514/6.2019-1569. en_US
dc.identifier.doi 10.2514/6.2019-1569. en_US
dc.identifier.uri http://hdl.handle.net/1853/61972
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original American Institute of Aeronautics and Astronautics
dc.relation.ispartofseries ASDL; en_US
dc.subject Convolutional neural networks en_US
dc.subject Inventory tracking en_US
dc.subject Unmanned aerial vehicles en_US
dc.subject Unmanned aircraft systems en_US
dc.title Multi-UAV Trajectory Optimization and Deep Learning-Based Imagery Analysis for a UAS-Based Inventory Tracking Solution en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
relation.isAuthorOfPublication d355c865-c3df-4bfe-8328-24541ea04f62
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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