Trace Pheromone-Based Energy-Efficient UAV Dynamic Coverage Using Deep Reinforcement Learning

X Cheng, R Jiang, H Sang, G Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) are widely used in disaster or remote areas to provide
ubiquitous service. Due to the limited energy and communication range of UAVs, and the …

Energy Constrained Multi-Agent Reinforcement Learning for Coverage Path Planning

C Zhao, J Liu, SU Yoon, X Li, H Li… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
For multi-agent area coverage path planning problem, existing researches regard it as a
combination of Traveling Salesman Problem (TSP) and Coverage Path Planning (CPP) …

UAV coverage path planning under varying power constraints using deep reinforcement learning

M Theile, H Bayerlein, R Nai, D Gesbert… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile
agent to travel over every point of an area of interest. We propose a new method to control …

Multi-UAV coverage path planning: a distributed online cooperation method

W Hu, Y Yu, S Liu, C She, L Guo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) plays a significant role
in intelligent distributed surveillance systems. However, due to poor cooperation, most …

Energy-efficient UAV control for effective and fair communication coverage: A deep reinforcement learning approach

CH Liu, Z Chen, J Tang, J Xu… - IEEE Journal on Selected …, 2018 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance
both the coverage and performance of communication networks in various scenarios, such …

[HTML][HTML] Energy-Efficient Multi-UAVs Cooperative Trajectory Optimization for Communication Coverage: An MADRL Approach

T Ao, K Zhang, H Shi, Z Jin, Y Zhou, F Liu - Remote Sensing, 2023 - mdpi.com
Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which
dynamically cover the wireless communication networks for Ground Users (GUs). The most …

Graph convolutional multi-agent reinforcement learning for UAV coverage control

A Dai, R Li, Z Zhao, H Zhang - 2020 International Conference …, 2020 - ieeexplore.ieee.org
Benefiting from the convenience of aerial flying, unmanned aerial vehicles (UAVs) can
provide linear accessibility and dynamic adjusted coverage scheme to ground users, thus …

Collaborative coverage path planning of UAV cluster based on deep reinforcement learning

Z Dong, C Liu - 2021 IEEE 3rd International Conference on …, 2021 - ieeexplore.ieee.org
With the continuous application of unmanned aerial vehicles (UAV) in the field of national
defense and civil use, the UAV cluster system in which multiple UAVs cooperate to perform …

Deep Reinforcement Learning Based UAVs Trajectory Optimization for Maximum Communication Coverage of Users

Y Zhao, X Sun, Y Liu, Q Yang - 2022 4th International …, 2022 - ieeexplore.ieee.org
With its flexibility and easy deployment, UAV can timely provide communication for areas
with damaged infrastructure. However, due to some constraints of the UAV itself, such as …

Transformer-Based Reinforcement Learning for Scalable Multi-UAV Area Coverage

D Chen, Q Qi, Q Fu, J Wang, J Liao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the
characteristics of flexible deployment and strong adaptability, which are an important …