This article focuses on enabling an aerial robot to fly through multiple openings at high speed using image-based estimation, planning, and control. State-of-the-art approaches assume that the robot’s global translational variables (e.g., position and velocity) can either be measured directly with external localization sensors or estimated onboard. Unfortunately, estimating the translational variables may be impractical because modeling errors and sensor noise can lead to poor performance. Furthermore, monocular-camera-based pose estimation techniques typically require a model of the gap (window) in order to handle the unknown scale. Herein, a new scheme for image-based estimation, aggressive-maneuvering trajectory generation, and motion control is developed for multi-rotor aerial robots. The approach described does not rely on measurement of the translational variables and does not require the model of the gap or window. First, the robot dynamics are expressed in terms of the image features that are invariant to rotation (invariant features). This step decouples the robot’s attitude and keeps the invariant features in the flat output space of the differentially flat system. Second, an optimal trajectory is efficiently generated in real time to obtain the dynamically-feasible trajectory for the invariant features. Finally, a controller is designed to enable real-time, image-based tracking of the trajectory. The performance of the estimation, planning, and control scheme is validated in simulations and through 80 successful experimental trials. Results show the ability to successfully fly through two narrow openings, where the estimation and planning computation and motion control from one opening to the next are performed in real time on the robot.

中文翻译：

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高速飞行通过多个开口的基于图像的估计、规划和控制

本文重点介绍使用基于图像的估计、规划和控制，使空中机器人能够高速飞越多个开口。最先进的方法假设机器人的全局平移变量（例如，位置和速度）可以直接用外部定位传感器测量或在机上估计。不幸的是，估计平移变量可能不切实际，因为建模错误和传感器噪声会导致性能不佳。此外，基于单目相机的姿态估计技术通常需要一个间隙（窗口）模型来处理未知的尺度。在此，为多旋翼航空机器人开发了一种基于图像的估计、主动机动轨迹生成和运动控制的新方案。所描述的方法不依赖于平移变量的测量，也不需要间隙或窗口的模型。首先，机器人动力学用对旋转不变的图像特征（不变特征）表示。这一步解耦了机器人的姿态，并在微分平坦系统的平坦输出空间中保持不变特征。其次，实时有效地生成最佳轨迹以获得不变特征的动态可行轨迹。最后，设计了一个控制器来实现对轨迹的实时、基于图像的跟踪。估计、规划和控制方案的性能在模拟和 80 次成功的实验中得到验证。结果显示能够成功飞过两个狭窄的开口，