Citation Export
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Park, Sanghyon | - |
| dc.contributor.author | Park, Jaeyoon | - |
| dc.contributor.author | Kim, Joongheon | - |
| dc.contributor.author | Jung, Soyi | - |
| dc.date.issued | 2022-01-01 | - |
| dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36812 | - |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143255479&origin=inward | - |
| dc.description.abstract | In this paper, we implemented a system that arrives at a target by avoiding complex installations with only the sensors mounted on the drone in an environment in which the drone operates in urban. The system is designed to operate autonomously using proximal policy optimization (PPO) reinforcement learning, and the simulation performance of autonomous drone operation according to various sensor conditions and various terrain complexity was analyzed. | - |
| dc.description.sponsorship | ACKNOWLEDGMENT This work was supported by Institute of Information communications Technology Planning Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2021-0-00794, Development of 3D Spatial Mobile Communication Technology). | - |
| dc.language.iso | eng | - |
| dc.publisher | IEEE Computer Society | - |
| dc.subject.mesh | Complex topographies | - |
| dc.subject.mesh | Condition | - |
| dc.subject.mesh | Deep reinforcement learning | - |
| dc.subject.mesh | Mobility sensors | - |
| dc.subject.mesh | Policy optimization | - |
| dc.subject.mesh | Proximal policy optimization | - |
| dc.subject.mesh | Reinforcement learnings | - |
| dc.subject.mesh | Simulation performance | - |
| dc.subject.mesh | Terrain complexity | - |
| dc.subject.mesh | Urban aerial mobility | - |
| dc.title | Deep Reinforcement Learning for Complex Topography in Urban Aerial Mobility: Sensor-based Calibration and Visualization | - |
| dc.type | Conference | - |
| dc.citation.conferenceDate | 2022.10.19. ~ 2022.10.21. | - |
| dc.citation.conferenceName | 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 | - |
| dc.citation.edition | ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation | - |
| dc.citation.endPage | 1203 | - |
| dc.citation.startPage | 1201 | - |
| dc.citation.title | International Conference on ICT Convergence | - |
| dc.citation.volume | 2022-October | - |
| dc.identifier.bibliographicCitation | International Conference on ICT Convergence, Vol.2022-October, pp.1201-1203 | - |
| dc.identifier.doi | 10.1109/ictc55196.2022.9952386 | - |
| dc.identifier.scopusid | 2-s2.0-85143255479 | - |
| dc.identifier.url | http://ieeexplore.ieee.org/xpl/conferences.jsp | - |
| dc.subject.keyword | Deep reinforcement learning | - |
| dc.subject.keyword | Proximal policy optimization (PPO) | - |
| dc.subject.keyword | Topography | - |
| dc.subject.keyword | Urban aerial mobility (UAM) | - |
| dc.type.other | Conference Paper | - |
| dc.description.isoa | false | - |
| dc.subject.subarea | Information Systems | - |
| dc.subject.subarea | Computer Networks and Communications | - |
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