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Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets

机译：利用人工神经网络跟踪运动目标的四旋翼基于视觉的控制

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摘要

This paper investigates the application of an image based visual servoing (IBVS) mechanism for controlling the translational and rotational movements of a quadrotor helicopter; via this scheme, the helicopter can track a moving target by means of a camera installed underneath. The image features chosen for this purpose are the perspective image features. A direct adaptive neural controller was designed to control transitional motion. The controller makes use of neural network controller radial basis function (RBF) to deal with the image dynamic uncertainties. These uncertainties relate to the depth of visual information and the movement of the considered target. Moreover, a sliding mode controller designed by using the image features has been presented for controlling the rotational movement of the helicopter. Also, due to the lack of accurate image velocity and the Euler angles, appropriate observers have been designed and used. The designed scheme needs no geometric information from the object followed by the quadrotor; therefore, this method can be employed in unknown environments. The simulation results for ideal and non-ideal conditions indicate that, in spite of the problems such as the uncertainty of the image depth and the mobility of the target, both in the rotational and translational motions, the helicopter has been able to reach the desired altitude and to properly track the moving target.
机译：本文研究了基于图像的视觉伺服（IBVS）机制在控制四旋翼直升机的平移和旋转运动中的应用。通过这种方案，直升机可以通过安装在下方的摄像机跟踪运动目标。为此选择的图像特征是透视图像特征。设计了直接自适应神经控制器来控制过渡运动。该控制器利用神经网络控制器的径向基函数（RBF）来处理图像动态不确定性。这些不确定性与视觉信息的深度和所考虑目标的运动有关。此外，已经提出了一种通过使用图像特征设计的滑模控制器，用于控制直升机的旋转运动。而且，由于缺乏精确的图像速度和欧拉角，已经设计并使用了合适的观察者。设计的方案不需要来自对象的几何信息，而后跟四旋翼。因此，该方法可以在未知环境中使用。理想和非理想条件下的仿真结果表明，尽管存在诸如旋转和平移运动中像深不确定性和目标移动性之类的问题，直升机仍能够达到所需高度并正确跟踪运动目标。

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