@article{Le2026, 
author = {Xiangyi Le and Deyu Lin and Yufei Zhao and Wang Miao and Yong Liang Guan},
title = {Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {104},
keywords = {Battery-free SWIPT-enabled sensor networks, multi-agent deep deterministic policy gradient, multi-unmanned aerial vehicle, collaborative energy charging, partially observable Markov decision process, centralized training with decentralized execution},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.083306},
doi = {10.32604/cmc.2026.083306},
abstract = {The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this paper. Specifically, we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements. To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments, the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process (POMDP). Subsequently, the Centralized Training with Decentralized Execution (CTDE) architecture of the MADDPG algorithm is leveraged to solve this POMDP, which effectively tackles the non-stationarity challenge inherent in multi-agent environments. Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability. It enables the adaptive emergence of spatial-division collaborative strategies, significantly enhances the average residual energy of the network, and elevates the node survival rate to nearly  90% . Compared with Deep Deterministic Policy Gradient (DDPG), the traditional static Partition-Greedy method, the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy, the proposed approach demonstrates substantial advantages.}
}