Reinforcement learning-based collision avoidance and optimal trajectory planning in UAV communication networks

YH Hsu, RH Gau - IEEE Transactions on Mobile Computing, 2020 - ieeexplore.ieee.org
In this paper, we propose a reinforcement learning approach of collision avoidance and
investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication …
被引用次数：98 相关文章 所有 3 个版本

Deep reinforcement learning approach for joint trajectory design in multi-UAV IoT networks

S Xu, X Zhang, C Li, D Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
被引用次数：30 相关文章

Intelligent trajectory design in UAV-aided communications with reinforcement learning

S Yin, S Zhao, Y Zhao, FR Yu - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
In this correspondence paper, we focus on a cellular network aided an unmanned aerial
vehicle (UAV) that serves as an aerial base station for multiple ground users. The UAV's …
被引用次数：101 相关文章 所有 2 个版本
[PDF] ieee.org

Adaptive UAV-trajectory optimization under quality of service constraints: A model-free solution

J Cui, Z Ding, Y Deng, A Nallanathan, L Hanzo - IEEE Access, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate
connectivity, are becoming a promising component of future wireless networks. A UAV …
被引用次数：47 相关文章 所有 8 个版本
[PDF] arxiv.org

Reinforcement learning in multiple-UAV networks: Deployment and movement design

X Liu, Y Liu, Y Chen - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
A novel framework is proposed for quality of experience driven deployment and dynamic
movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex …
被引用次数：303 相关文章 所有 5 个版本
[PDF] ieee.org

UAV path planning based on multi-layer reinforcement learning technique

Z Cui, Y Wang - Ieee Access, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) have been widely used in many applications due to its
small size, swift mobility and low cost. Therefore, the study of guidance, navigation and …
被引用次数：52 相关文章 所有 2 个版本
[PDF] arxiv.org

Learning-based UAV path planning for data collection with integrated collision avoidance

X Wang, MC Gursoy, T Erpek… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks,
and determining collision-free trajectory in multi-UAV noncooperative scenarios while …
被引用次数：53 相关文章 所有 4 个版本
[PDF] arxiv.org

Reinforcement learning for decentralized trajectory design in cellular UAV networks with sense-and-send protocol

J Hu, H Zhang, L Song - IEEE Internet of Things Journal, 2018 - ieeexplore.ieee.org
Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing
applications over cellular networks. The performance of a UAV is determined by both its …
被引用次数：114 相关文章 所有 4 个版本
[PDF] ieee.org

Multi-agent reinforcement learning-based resource allocation for UAV networks

J Cui, Y Liu, A Nallanathan - IEEE Transactions on Wireless …, 2019 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for
providing both cost-effective and on-demand wireless communications. This article …
被引用次数：455 相关文章 所有 5 个版本

Three-dimension trajectory design for multi-UAV wireless network with deep reinforcement learning

W Zhang, Q Wang, X Liu, Y Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The effective trajectory design of multiple unmanned aerial vehicles (UAVs) is investigated
for improving the capacity of the communication system. The aim is for maximizing real-time …
被引用次数：42 相关文章