2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6721-6734· 0 citations· 35 references
Computer Science
Abstract
Unmanned Aerial Vehicles (UAVs) are pivotal for facilitating data collection in emergency scenarios. Despite the potential of Multi-Agent Deep Reinforcement Learning (MADRL) in coordinating such systems, existing researches struggle to resolve the high-dimensional coupling of data collection, trajectory planning, and energy scheduling under strict collision avoidance and Return-To-Base (RTB) constraints. This paper proposes a energy-aware cooperative MADRL framework designed to maximize data collection utility under energy constraints. Specifically, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach featuring a Centralized Training with Decentralized Execution (CTDE) design and a multi-objective reward mechanism to balance conflicting optimization goals. Extensive simulations validate the advantages of the proposed framework over leading baselines. Notably, the algorithm exhibits significant quantitative advantages in complex high-load scenarios. These outcomes prove that our method achieves higher task completion rates while strictly adhering to RTB and safety protocols.
This paper investigates the problem of cooperative multiple unmanned aerial vehicles (UAVs) data collection for Internet of Things (IoT) networks in dense urban environments. Unlike existing studies that predominantly rely on idealized spatial models and average-based probabilistic channel models, this work explicitly...
Haitao Chen, Xinfeng Deng, Zhe Wang et al.· IEEE Transactions on Cogniti...· 0 citations
A multi-agent guided soft actor–critic (MAGSAC) deep reinforcement learning algorithm to enable multiple UAVs to simultaneously arrive at multiple constant-velocity moving targets and outperforms existing mainstream algorithms in synchronization success rate, temporal synchronization accuracy, and safety.
Shuanli Jia, Naiming Qi, Zheng Li et al.· Drones· 0 citations
Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory pla...
Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen et al.· arXiv.org· 0 citations
A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.
W. Saber, Hanan Algamil, Fifi Farouk et al.· Future Internet· 0 citations
A cooperative guidance law based on the experience-guided multi-agent proximal policy optimization (E-MAPPO) algorithm is proposed for multiple unmanned aerial vehicles (UAVs) to track dynamic points of interest in civilian applications and results indicate that the proposed method generalizes well to different types o...
Hao Xiong, Minghu Tan, Xiaoyu Liu et al.· Drones· 0 citations
