Name
Title | ||
|---|---|---|
Deep Reinforcement Learning Approach for Joint Trajectory Design in Multi-UAV IoT Networks |
Abstract | ||
|---|---|---|
In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV's maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TVT.2022.3144277 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY |
Keywords | DocType | Volume |
Trajectory, Task analysis, Interference, Optimization, Trajectory planning, Data collection, Collision avoidance, Multi-UAV, trajectory design, multi-agent Deep Re- inforcement Learning | Journal | 71 |
Issue | ISSN | Citations |
3 | 0018-9545 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Xu | 1 | 0 | 0.34 |
| Xiangyu Zhang | 2 | 0 | 0.34 |
| Chunguo Li | 3 | 48 | 10.72 |
| Dongming Wang | 4 | 571 | 59.66 |
| Luxi Yang | 5 | 1180 | 118.08 |