.png){kind=icon}
返回
Learning-Based UAV Path Planning for Data Collection With Integrated Collision Avoidance
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2022.3153585.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this article, we consider a path-planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV noncooperative scenarios involve a random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep Q-network (D3QN) is proposed to learn the decision-making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.
Keyword:
Collision avoidance
data collection
deep reinforcement learning (RL)
multiunmanned aerial vehicle (UAV) scenarios
path planning
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
分享收藏分享收藏DRLR: A Deep-Reinforcement-Learning-Based Recruitment Scheme for Massive Data Collections in 6G-Based IoT NetworksDRLR: 一种基于深度强化学习的面向6g物联网海量数据采集的招募方案
PREAI
分享收藏Lipid-Binding Activity of Intrinsically Unstructured Cytoplasmic Domains of Multichain Immune Recognition Receptor Signaling Subunits.png)
Biochemistry
OAAI
BiochemistryIF0
OAAI分享收藏Effects of nickel and indium ternary additions on the hydrogenation of MgAl intermetallic compounds
PREAI
分享收藏UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices面向时间受限物联网设备数据采集的无人机轨迹规划
PREAI
分享收藏
查看更多内容