Energy-Aware Multi-UAV Collaboration for Data Collection and Trajectory Planning With MADDPG

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.