State-of-the-art and future research challenges in uav swarms
Due to their potential to accomplish complicated missions more effectively, UAV swarms
have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved …
have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved …
[HTML][HTML] Q-learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments
Path Planning methods for the autonomous control of Unmanned Aerial Vehicle (UAV)
swarms are on the rise due to the numerous advantages they bring. There are increasingly …
swarms are on the rise due to the numerous advantages they bring. There are increasingly …
Model-reference reinforcement learning for safe aerial recovery of unmanned aerial vehicles
B Zhao, M Huo, Z Yu, N Qi, J Wang - Aerospace, 2023 - mdpi.com
In this study, we propose an aerial rendezvous method to facilitate the recovery of
unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for …
unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for …
Path planning for heterogeneous uavs with radar sensors
Due to their flexibility and agility, unmanned aerial vehicles (UAVs) offer a promising
approach to cluster planning within wireless sensor networks (WSNs). However, the limited …
approach to cluster planning within wireless sensor networks (WSNs). However, the limited …
A federated learning latency minimization method for uav swarms aided by communication compression and energy allocation
L Zeng, W Wang, W Zuo - Sensors, 2023 - mdpi.com
Unmanned aerial vehicle swarms (UAVSs) can carry out numerous tasks such as detection
and mapping when outfitted with machine learning (ML) models. However, due to the flying …
and mapping when outfitted with machine learning (ML) models. However, due to the flying …
[HTML][HTML] Reinforcement learning-based dynamic pruning for distributed inference via explainable AI in healthcare IoT systems
Abstract Deep Neural Networks (DNNs) have become the key technique to revolutionize the
healthcare sector. However, conducting online remote inference is often impractical due to …
healthcare sector. However, conducting online remote inference is often impractical due to …
SoftWind: Software-defined trajectory correction modelling of gust wind effects on internet of drone things using glowworm swarm optimization
A Hazra, D De - Ad Hoc Networks, 2024 - Elsevier
The dynamic nature of the atmosphere, especially wind gust, poses a crucial challenge to
efficient and real-time drone operations. This article presents a novel MQTT based software …
efficient and real-time drone operations. This article presents a novel MQTT based software …
Age of Information Aware Trajectory Planning of UAV
J Pan, Y Li, R Chai, S Xia, L Zuo - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This paper investigates the planning of Unmanned aerial vehicles (UAVs) trajectory in UAV-
assisted Internet of Things (IoT) networks with a massive number of IoT devices (IoTDs) …
assisted Internet of Things (IoT) networks with a massive number of IoT devices (IoTDs) …
Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas
Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention
due to their potential applications in emergency-response applications, including wireless …
due to their potential applications in emergency-response applications, including wireless …
Multi-UAV Adaptive Cooperative Formation Trajectory Planning Based on An Improved MATD3 Algorithm of Deep Reinforcement Learning
X Xing, Z Zhou, Y Li, B Xiao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Multi-unmanned aerial vehicle (multi-UAV) cooperative trajectory planning is an extremely
challenging issue in UAV research field due to its NP-hard characteristic, collision avoiding …
challenging issue in UAV research field due to its NP-hard characteristic, collision avoiding …