Unified 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 with the current state-of-the-art is not possible due to the complexity of the models used. In this paper, we present a systematic approach for real-time identification and tuning of visual servoing systems based on a novel robustified version of the recent deep neural networks with the modified relay feedback test (DNN-MRFT) approach. The proposed robust DNN-MRFT algorithm can be used with a multitude of vision sensors and estimation algorithms despite the high levels of sensor's noise. Sensitivity of MRFT to perturbations is investigated and its effect on identification and tuning performance is analyzed. DNN-MRFT was able to detect performance changes due to the use of slower vision sensors, or due to the integration of accelerometer measurements. Experimental identification results were closely matching simulation results, which can be used to explain system behaviour and anticipate the closed loop performance limits given a certain hardware and software setup. Finally, we demonstrate the capability of the DNN-MRFT tuned visual servoing systems to reject external disturbances. Some advantages of the suggested robust identification approach compared to existing visual servoing design approaches are presented.

Dynamic Based Estimator for UAVs with Real-time Identification Using DNN and the Modified Relay Feedback Test

Control performance of Unmanned Aerial Vehicles (UAVs) is directly affected by their ability to estimate their states accurately. With the increasing popularity of autonomous UAV solutions in real world applications, it is imperative to develop robust adaptive estimators that can ameliorate sensor noises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimation can provide significant advantages, but remains challenging due to the complex and expensive pre-flight experiments required to obtain UAV dynamic parameters. In this paper, we propose two decoupled dynamic model based Extended Kalman Filters for UAVs, that provide high rate estimates for position, and velocity of rotational and translational states, as well as filtered inertial acceleration. The dynamic model parameters are estimated online using the Deep Neural Network and Modified Relay Feedback Test (DNN-MRFT) framework, without requiring any prior knowledge of the UAV physical parameters. The designed filters with real-time identified process model parameters are tested experimentally and showed two advantages. Firstly, smooth and lag-free estimates of the UAV rotational speed and inertial acceleration are obtained, and used to improve the closed loop system performance, reducing the controller action by over 6 %. Secondly, the proposed approach enabled the UAV to track aggressive trajectories with low rate position measurements, a task usually infeasible under those conditions. The experimental data shows that we achieved estimation performance matching other methods that requires full knowledge of the UAV parameters.