Breaking the Pre-Planning Barrier: Adaptive Real-Time Coordination of Heterogeneous UAVs

TL;DR

This paper introduces HGAM, a novel reinforcement learning framework that enables real-time, decentralized coordination of heterogeneous UAVs, significantly improving data collection and charging efficiency in dynamic environments.

Contribution

The paper presents HGAM, a new multi-agent reinforcement learning approach that facilitates adaptive, decentralized coordination of heterogeneous UAVs using local graph-based observations.

Findings

30% improvement in data collection coverage

20% increase in charging efficiency

Superior performance over existing methods

Abstract

Unmanned Aerial Vehicles (UAVs) offer significant potential in dynamic, perception-intensive tasks such as search and rescue and environmental monitoring; however, their effectiveness is severely restricted by conventional pre-planned routing methods, which lack the flexibility to respond in real-time to evolving task demands, unexpected disturbances, and localized view limitations in real-world scenarios. To address this fundamental limitation, we introduce a novel multi-agent reinforcement learning framework named \textbf{H}eterogeneous \textbf{G}raph \textbf{A}ttention \textbf{M}ulti-agent Deep Deterministic Policy Gradient (HGAM), uniquely designed to enable adaptive real-time coordination between mission UAVs (MUAVs) and charging UAVs (CUAVs). HGAM specifically addresses the previously unsolved challenge of enabling precise, decentralized continuous-action coordination solely based on local, heterogeneous graph-based observations. Extensive simulations demonstrate that HGAM substantially surpasses existing methods, achieving, for example, a 30\% improvement in data collection coverage and a 20\% increase in charging efficiency, providing crucial insights and foundations for the future deployment of intelligent, flexible UAV networks in complex, dynamic environments.