用扩散模型学习无人机动力学，提升复杂场景下的飞行鲁棒性。
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
- 将无人机动力学建模为序列生成任务，用条件扩散模型捕捉不确定性
- 在模拟与真实飞行中均实现对未知路径、载荷和风扰的稳定追踪
- 适合需要高鲁棒性的无人机控制研究者或工程应用团队
四旋翼系统固有的脆弱性源于模型不准确和外部干扰，这些因素会降低性能并影响系统稳定性，使精确控制变得困难。现有基于模型的方法或作确定性假设，或使用高斯分布表示不确定性，或依赖典型模型，往往难以捕捉真实动力学中复杂的多模态特性。本文提出DroneDiffusion，一种利用条件扩散模型学习四旋翼动力学的新框架，将动力学建模视为序列生成任务。该方法通过捕捉不确定性的时序特性，有效缓解误差传播，实现对未见复杂场景的优异泛化能力。我们还将学习到的动力学集成至自适应控制器，实现轨迹跟踪并保证稳定性。大量仿真与真实飞行实验表明，该框架在各种场景下表现出强鲁棒性，包括陌生飞行路径、不同载荷、速度及风扰。
原文摘要 · Abstract (English)
An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, making precise control challenging. Existing model-based approaches either make deterministic assumptions, utilize Gaussian-based representations of uncertainty, or rely on nominal models, all of which often fall short in capturing the complex, multimodal nature of real-world dynamics. This work introduces DroneDiffusion, a novel framework that leverages conditional diffusion models to learn quadrotor dynamics, formulated as a sequence generation task. DroneDiffusion achieves superior generalization to unseen, complex scenarios by capturing the temporal nature of uncertainties and mitigating error propagation. We integrate the learned dynamics with an adaptive controller for trajectory tracking with stability guarantees. Extensive experiments in both simulation and real-world flights demonstrate the robustness of the framework across a range of scenarios, including unfamiliar flight paths and varying payloads, velocities, and wind disturbances.
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