利用深神经网络进行可视振荡应用的噪音耐力识别和调试方法 (Noise Tolerant Identification and Tuning Approach Using Deep Neural Networks For Visual Servoing Applications)

Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability of low-cost on-board sensors and computers. Tuning such systems to work properly requires extensive domain specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits of UAV based visual servoing is difficult due to the complexity of the models used. In this paper, we propose a novel noise tolerant approach for real-time identification and tuning of visual servoing systems, based on deep neural networks (DNN) classification of system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite the high levels of sensor's noise. Response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes due to the use of high latency vision sensors, or due to the integration of inertial measurement unit (IMU) measurements in the UAV states estimation. Experimental identification closely matches simulation results, which can be used to explain system behaviour and predict the closed loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT tuned UAVs to reject external disturbances like wind, or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in literature.

翻译：由于提供了低成本的机载传感器和计算机,对无人驾驶飞行器(无人驾驶飞行器)进行了基于视觉的控制,这些应用范围很广,因此采用了基于视觉的控制; 使这些系统正常运行需要广泛的具体领域经验,这限制了新兴应用程序的增长; 此外,由于所用模型的复杂性,很难获得无人驾驶飞行器的视觉传感器的性能限制; 在本文中,我们提议根据经修改的中继反馈测试(MRFT)产生的系统反应的深层神经网络分类(DNNN),对实时识别和调控视觉传感器系统采用新的噪音容忍办法; 拟议的方法称为DNNNN(DN)系统,使用保护噪音的MRFT(DNNNN-NP-MRFT)系统,使用这种称为DNNNN(DNNNN)系统,使用这种方法可以使用多种视觉传感器和估算算法,尽管传感器的噪音噪音非常高; 调查DNNNP-NP-MF(D)对声音系统使用高清晰度测算法,也可以探测性性工作情况,在进行升级测测算; 将LAVAF-LAF-LA-L-LA-L-L-L-L-L-L-L-L-L-L-LL-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L