| Title |
Autonomous Aerial Mobility Learning for Drone-Taxi Flight Control |
| ID_Doc |
41601 |
| Authors |
Yun, WJ; Ha, YJ; Jung, S; Kim, J |
| Title |
Autonomous Aerial Mobility Learning for Drone-Taxi Flight Control |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/ICTC52510.2021.9620751 |
| Abstract |
In smart city scenarios, the use of unmanned aerial vehicle (UAV) networks is one of actively discussed technologies. In this paper, we consider the scenario where carpoolable UAV-based drone taxis configure their optimal routes to deliver packages and passengers in an autonomous and efficient way. In order to realize this application with drone-taxi UAV networks, a multi-agent deep reinforcement learning (MADRL) based algorithm is designed and implemented for the optimal route configuration. In the corresponding MADRL formulation, the drone-taxi related states, actions, and rewards are defined in this paper. Lastly, we confirm that our proposed algorithm achieves desired results. |
| Author Keywords |
Smart City; Unmanned Aerial Vehicle (UAV); Drone-Taxi; Reinforcement Learning; Multi-Agent Deep Reinforcement Learning (MADRL) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000790235800079 |
| WoS Category |
Engineering, Electrical & Electronic |
| Research Area |
Engineering |
| PDF |
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