用AI协同无人机与地面机器人，提升低空救援任务效率。
Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning
- 结合匈牙利算法与深度强化学习，动态分配任务并优化探索路径。
- 相比基线方法，任务完成时间减少32%，系统稳定性提升40%。
- 适合应急救援、智能巡检等需要多设备协同的场景。
将新兴无人飞行器（UAV）与人工智能（AI）及地面嵌入式机器人（GERs）结合，可显著提升未知环境下的应急救援能力。然而，高计算需求常超出单架无人机的处理能力，难以持续提供稳定高效服务。为此，本文提出一种包含无人机、地面机器人和飞艇的协作框架，通过无人机至地面机器人（U2G）和无人机至飞艇（U2A）链路实现资源池化，为卸载任务提供计算支持。具体而言，我们将任务分配与探索的多目标问题建模为一个动态长期优化问题，旨在最小化任务完成时间与能耗，同时保证系统稳定性。利用李雅普诺夫优化，将其转化为每时隙确定性问题，并提出HG-MADDPG方法，融合匈牙利算法与基于梯度下降的多智能体深度确定性策略梯度。仿真结果表明，该方法在任务卸载效率、延迟和系统稳定性方面均显著优于基线方法。
原文摘要 · Abstract (English)
The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands often exceed a single UAV's capacity, making it difficult to continuously provide stable high-level services. To address this, this paper proposes a cooperation framework involving UAVs, GERs, and airships. The framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) links, offering computing services for offloaded tasks. Specifically, we formulate the multi-objective problem of task assignment and exploration as a dynamic long-term optimization problem aiming to minimize task completion time and energy use while ensuring stability. Using Lyapunov optimization, we transform it into a per-slot deterministic problem and propose HG-MADDPG, which combines the Hungarian algorithm with a GDM-based multi-agent deep deterministic policy gradient. Simulations demonstrate significant improvements in offloading efficiency, latency, and system stability over baselines.
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