跳转至

文章背景与核心概要

本文探讨了将扩散模型（Diffusion Models, DMs）这一新兴的生成式AI类别集成到无人机（UAV）系统中的技术方案，旨在突破传统无人机在决策和数字建模方面的局限。在当前的无人机通信网络中，虽然强化学习（RL）和数字孪生（DT）得到了广泛应用，但它们常常面临样本效率低、数据多功能性受限以及数据需求量大等痛点。扩散模型通过直接从训练数据中学习底层概率分布，而非局限于分类边界，成功解决了数据稀缺问题，提高了建模准确性并生成了逼真的场景。

仿真实验证明了扩散模型在实际应用中的高效性，特别是在利用深度强化学习（DRL）进行四无人机集群协同任务时，能够准确生成邻近无人机的速度估计。这项研究展示了扩散模型与RL及DT技术相结合的巨大潜力，为未来智能化、数据驱动的无人机运维开辟了新途径。

Diffusion Models for Smarter UAVs: Decision-Making and Modeling

Executive Summary

This paper investigates the integration of Diffusion Models (DMs)—a novel class of generative AI—into Uncrewed Aerial Vehicle (UAV) systems to overcome traditional limitations in decision-making and digital modeling. While Reinforcement Learning (RL) and Digital Twins (DTs) are widely used in UAV communication networks, they frequently suffer from low sample efficiency, limited data versatility, and high data requirements. By learning underlying probability distributions directly from training data rather than focusing solely on class boundaries, DMs successfully address data scarcity, improve modeling accuracy, and generate realistic scenarios. Simulations demonstrate the practical efficacy of DMs in generating accurate neighbor velocity estimates for a four-UAV swarm coordination task utilizing Deep Reinforcement Learning (DRL).

本文研究了将作为一类新兴生成式AI的扩散模型（Diffusion Models, DMs）集成到无人机（UAV）系统中的方案，以克服传统决策和数字建模的局限性。尽管强化学习（RL）和数字孪生（DT）广泛应用于无人机通信网络，但它们经常面临样本效率低、数据多样性受限以及数据需求量大的问题。通过直接从训练数据中学习底层概率分布，而不是仅仅关注分类边界，扩散模型成功解决了数据稀缺问题，提高了建模准确性并生成了逼真的场景。仿真结果证明了DMs在利用深度强化学习（DRL）进行四无人机集群协同任务时，生成准确邻近速度估计的实际效能。

Paper Metadata

论文元数据

Abstract

Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital modeling continue to hinder their rapid development. Reinforcement Learning (RL) algorithms face limitations such as low sample efficiency and limited data versatility, which are further amplified in UAV communications scenarios.

Additionally, Digital Twin (DT) modeling presents significant challenges in decision-making and data management. RL models, often integrated into DT frameworks to address these issues, require large amounts of training data to make accurate predictions. Unlike traditional approaches that focus on class boundaries, Diffusion Models (DMs)—a new class of generative AI—learn the underlying probability distribution from training data and can generate reliable new patterns based on this learned distribution.

DT and RL have complementary roles in enabling intelligent, data-driven UAV operations. DMs further enhance this synergy by addressing data scarcity, improving modeling accuracy, and generating realistic scenarios, which benefit both DT simulations and RL training. In this paper, we explore the integration of DMs with RL and DT. Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL).

无人机（UAV）在现代通信网络中的应用日益广泛。然而，决策和数字建模方面的挑战继续阻碍其快速发展。强化学习（RL）算法面临样本效率低和数据多功能性受限等局限性，在无人机通信场景中这些问题会被进一步放大。

此外，数字孪生（DT）建模在决策和数据管理方面也带来了严峻挑战。为解决这些问题通常集成到DT框架中的RL模型，需要大量的训练数据来进行准确预测。与专注于分类边界的传统方法不同，作为一类新兴生成式AI的扩散模型（DMs）从训练数据中学习潜在的概率分布，并能基于这种学到的分布生成可靠的新模式。

DT和RL在实现智能、数据驱动的无人机运营方面发挥着互补作用。DMs通过解决数据稀缺、提高建模准确性以及生成逼真的场景，进一步增强了这种协同效应，从而使DT仿真和RL训练双受其益。在本文中，我们探讨了DMs与RL和DT的集成。仿真结果证实了DMs在利用深度强化学习（DRL）进行四无人机集群协同任务时生成邻近速度估计的有效性和优势。

Access & Resources

访问与资源