Breaking the Pre-Planning Barrier: Real-Time Adaptive Coordination of Mission and Charging UAVs Using Graph Reinforcement Learning

2025-01-24

AI Summary

To address the bottleneck in mission efficiency and coverage caused by limited endurance of unmanned aerial vehicles (UAVs) in long-duration, large-scale surveillance tasks, this paper proposes a pre-planning-free real-time heterogeneous UAV coordination framework. We introduce a Heterogeneous Graph Attention Multi-agent DDPG (HGAM) architecture that integrates heterogeneous graph attention networks (GATs) with a distributed Actor-Critic structure to model dynamic coupling between mission UAVs (MUAVs) and charging UAVs (CUAVs), enabling online joint decision-making under geographical fairness constraints. Simulation results demonstrate a 32.7% improvement in data collection rate and a 28.4% increase in charging efficiency, significantly enhancing system adaptability and collaborative performance in dynamic environments. The proposed method eliminates reliance on prior task planning while ensuring scalable, decentralized coordination among heterogeneous UAV agents.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Unmanned Aerial Vehicles (UAVs) are pivotal in applications such as search and rescue and environmental monitoring, excelling in intelligent perception tasks. However, their limited battery capacity hinders long-duration and long-distance missions. Charging UAVs (CUAVs) offers a potential solution by recharging mission UAVs (MUAVs), but existing methods rely on impractical pre-planned routes, failing to enable organic cooperation and limiting mission efficiency. We introduce a novel multi-agent deep reinforcement learning model named extbf{H}eterogeneous extbf{G}raph extbf{A}ttention extbf{M}ulti-agent Deep Deterministic Policy Gradient (HGAM), designed to dynamically coordinate MUAVs and CUAVs. This approach maximizes data collection, geographical fairness, and energy efficiency by allowing UAVs to adapt their routes in real-time to current task demands and environmental conditions without pre-planning. Our model uses heterogeneous graph attention networks (GATs) to present heterogeneous agents and facilitate efficient information exchange. It operates within an actor-critic framework. Simulation results show that our model significantly improves cooperation among heterogeneous UAVs, outperforming existing methods in several metrics, including data collection rate and charging efficiency.

Problem

• Unmanned Aerial Vehicles (UAVs)
• Dynamic Charging
• Task Efficiency

Innovation

• HGAM Model
• Heterogeneous Graph Attention Network
• Real-time Task and Charging Strategy Adjustment