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.

翻译：四旋翼系统固有的脆弱性源于模型不准确性和外部扰动。这些因素会阻碍系统性能并损害其稳定性，使得精确控制具有挑战性。现有的基于模型的方法要么做出确定性假设，要么采用基于高斯分布的不确定性表征，或依赖于标称模型，这些方法通常难以捕捉现实世界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型框架，该框架将动力学学习构建为序列生成任务。通过捕捉不确定性的时序特性并减轻误差传播，DroneDiffusion在未见过的复杂场景中实现了卓越的泛化能力。我们将学习到的动力学模型与自适应控制器相结合，用于具有稳定性保证的轨迹跟踪。在仿真和真实飞行中的大量实验表明，该框架在包括陌生飞行路径、变化载荷、不同速度及风扰在内的多种场景下均展现出优异的鲁棒性。