论文标题
直接NMPC用于固定周无人机的后台运动计划
Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs
论文作者
论文摘要
在速度，耐力和效率方面，固定翼无人机（UAV）在旋转翼无人机上具有显着的性能优势。但是，传统上，这些车辆在可操作性方面受到严重限制。在本文中，我们提出了一种非线性控制方法，用于使特技固定翼无人机在约束空间中操纵。我们的方法利用全州直接轨迹优化和简约但具有代表性的非线性飞机模型来计划在高攻击中以5 Hz实时计划积极的固定翼轨迹。随机运动计划用于避免本地最小值，并使用局部线性反馈来补偿更新之间的模型不准确性。我们证明了我们在硬件中的方法，并表明在存在模型不确定性的情况下成功导航复杂环境都是必要的。
Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.
