This study aims to address the key challenges in multi-unmanned aerial vehicle (UAV) data sensing systems, where energy-constrained UAVs require real-time trajectory optimization to simultaneously maximize coverage efficiency and minimize energy consumption. Traditional centralized optimization approaches struggle with scalability and computational complexity due to the non-convexity and high dimensionality of the joint optimization problem. To overcome these limitations, the authors propose a distributed multi-agent deep reinforcement learning (MADRL) framework that leverages the autonomous decision-making capability of deep reinforcement learning to achieve distributed continuous action control of UAVs, thereby enabling efficient autonomous deployment.
This study proposes a multi-UAV energy-efficient data collection scheme (multi-UAV E2DC) based on an MADRL algorithm. The proposed approach enables UAVs to dynamically collect data from multiple ground sensors while accounting for practical constraints such as communication range, motion limits and energy consumption. To achieve this, the authors first construct a multi-objective optimization model by integrating an air-to-ground communication model with a UAV energy consumption model. Building on this foundation, the authors further develop an enhanced MADRL algorithm within a centralized training and decentralized execution (CTDE) actor-critic framework, which supports efficient continuous trajectory control and deployment of multi-UAV.
Extensive simulations demonstrate that the proposed approach achieves superior performance in multi-objective optimization compared to benchmark methods, including random policy, K-means clustering and multi-agent deep deterministic policy gradient. Specifically, the proposed method outperforms in terms of average coverage density, data volume and coverage energy efficiency index.
This study proposes an MADRL-based energy-efficient data collection framework for UAVs, which integrates air-to-ground communication and UAV energy consumption models to formulate a multi-objective optimization problem. By adopting a CTDE framework for continuous trajectory control, it effectively overcomes the computational challenges of traditional non-convex optimization methods in complex environments. The proposed approach offers a theoretically sound and practically applicable solution for distributed UAV sensing in extreme disaster scenarios.
