2021/07/04 by Oussama Abdul Hay, Hay, Oussama Abdul, Mohamad Chehadeh +12 · 1 citation
Computer Science · Engineering · #Advanced Vision and Imaging #Artificial intelligence #Artificial neural network #CCD and CMOS Imaging Sensors #Computer science #Computer vision #Data modeling #FOS: Computer and information sciences #FOS: Electrical engineering #Identification (biology) #Image (mathematics) #Inertial measurement unit #Noise (video) #Robot #Robotics (cs.RO) #Robotics and Sensor-Based Localization #System identification #Systems and Control (eess.SY) #Visual servoing #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2107.01581
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/07/04 · arxiv created 2022/02/08 · arxiv updated 2022/02/09 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/08
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.