Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications

Authors
Yun, W.J.Jung, S.Kim, J.Kim, J.-H.
Issue Date
3월-2021
Publisher
Korean Institute of Communication Sciences
Keywords
Air taxi; Distributed deep reinforcement learning; Drone taxi; eVTOL; Urban aerial mobility
Citation
ICT Express, v.7, no.1, pp.1 - 4
Indexed
SCIE
SCOPUS
KCI
Journal Title
ICT Express
Volume
7
Number
1
Start Page
1
End Page
4
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/129142
DOI
10.1016/j.icte.2021.01.005
ISSN
2405-9595
Abstract
The urban aerial mobility (UAM) system, such as drone taxi or air taxi, is one of future on-demand transportation networks. Among them, electric vertical takeoff and landing (eVTOL) is one of UAM systems that is for identifying the locations of passengers, flying to the positions where the passengers are located, loading the passengers, and delivering the passengers to their destinations. In this paper, we propose a distributed deep reinforcement learning where the agents are formulated as eVTOL vehicles that can compute the optimal passenger transportation routes under the consideration of passenger behaviors, collisions among eVTOL, and eVTOL battery status. © 2021 The Korean Institute of Communications and Information Sciences (KICS)
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Electrical Engineering > 1. Journal Articles

qrcode

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

Related Researcher

Researcher Kim, Joong heon photo

Kim, Joong heon
공과대학 (전기전자공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE