用强化学习让可倾斜旋翼无人机更敏捷、更抗干扰地完成全方位飞行。
Learning Agile and Robust Omnidirectional Aerial Motion on Overactuated Tiltable-Quadrotors
- 通过强化学习协调旋翼与关节动作，实现精准三维姿态控制。
- 在真实硬件上零样本部署，六自由度追踪精度媲美顶尖模型预测控制。
- 结合系统辨识与物理一致的域随机化，提升仿真到现实的迁移可靠性。
可倾斜旋翼飞行器通过推力矢量实现全向机动，但关节与旋翼动力学强耦合带来显著控制挑战。基于模型的控制器在理想条件下精度高，但在扰动和模型不确定性下鲁棒性与响应能力下降。本文研究面向过驱动可倾斜四旋翼机的强化学习控制方法，旨在提升运动的敏捷性与鲁棒性。提出一种学习型控制框架，高效获取旋翼-关节协同行为，实现对 $SE(3)$ 空间中目标位姿的精确到达。为保障可靠仿真到现实的迁移并保持运动精度，融合系统辨识与最小且物理一致的域随机化策略。相较于最先进的非线性模型预测控制（NMPC）方法，该方法在六自由度姿态跟踪精度相当的同时，展现出更强的鲁棒性与任务泛化能力，支持零样本直接部署于真实硬件平台。
原文摘要 · Abstract (English)
Tilt-rotor aerial robots enable omnidirectional maneuvering through thrust vectoring, but introduce significant control challenges due to the strong coupling between joint and rotor dynamics. While model-based controllers can achieve high motion accuracy under nominal conditions, their robustness and responsiveness often degrade in the presence of disturbances and modeling uncertainties. This work investigates reinforcement learning for omnidirectional aerial motion control on over-actuated tiltable quadrotors that prioritizes robustness and agility. We present a learning-based control framework that enables efficient acquisition of coordinated rotor-joint behaviors for reaching target poses in the $SE(3)$ space. To achieve reliable sim-to-real transfer while preserving motion accuracy, we integrate system identification with minimal and physically consistent domain randomization. Compared with a state-of-the-art NMPC controller, the proposed method achieves comparable six-degree-of-freedom pose tracking accuracy, while demonstrating superior robustness and generalization across diverse tasks, enabling zero-shot deployment on real hardware.
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