Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.startPage | e1119 | - |
dc.citation.title | PEERJ COMPUTER SCIENCE | - |
dc.citation.volume | 8 | - |
dc.contributor.author | Jung, Minjae | - |
dc.contributor.author | Oh, Hyondong | - |
dc.date.accessioned | 2023-12-21T13:37:06Z | - |
dc.date.available | 2023-12-21T13:37:06Z | - |
dc.date.created | 2022-12-05 | - |
dc.date.issued | 2022-10 | - |
dc.description.abstract | Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance. | - |
dc.identifier.bibliographicCitation | PEERJ COMPUTER SCIENCE, v.8, pp.e1119 | - |
dc.identifier.doi | 10.7717/peerj-cs.1119 | - |
dc.identifier.issn | 2376-5992 | - |
dc.identifier.scopusid | 2-s2.0-85141746225 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/60116 | - |
dc.identifier.wosid | 000888476600002 | - |
dc.language | 영어 | - |
dc.publisher | PEERJ INC | - |
dc.title | Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Mission planning | - |
dc.subject.keywordAuthor | Deep reinforcement learning | - |
dc.subject.keywordAuthor | Vehicle routing problem | - |
dc.subject.keywordAuthor | Attention mechanism | - |
dc.subject.keywordAuthor | Neural Networks | - |
dc.subject.keywordPlus | VARIABLE NEIGHBORHOOD SEARCH | - |
dc.subject.keywordPlus | ROUTING PROBLEM | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.