Ajou University repository

Deep Reinforcement Learning for Complex Topography in Urban Aerial Mobility: Sensor-based Calibration and Visualization
  • Park, Sanghyon ;
  • Park, Jaeyoon ;
  • Kim, Joongheon ;
  • Jung, Soyi
Citations

SCOPUS

0

Citation Export

Publication Year
2022-01-01
Journal
International Conference on ICT Convergence
Publisher
IEEE Computer Society
Citation
International Conference on ICT Convergence, Vol.2022-October, pp.1201-1203
Keyword
Deep reinforcement learningProximal policy optimization (PPO)TopographyUrban aerial mobility (UAM)
Mesh Keyword
Complex topographiesConditionDeep reinforcement learningMobility sensorsPolicy optimizationProximal policy optimizationReinforcement learningsSimulation performanceTerrain complexityUrban aerial mobility
All Science Classification Codes (ASJC)
Information SystemsComputer Networks and Communications
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36812
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143255479&origin=inward
DOI
https://doi.org/10.1109/ictc55196.2022.9952386
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
Funding
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).
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.