This paper is concerned with the design and implementation of a distributed proportional-derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi-Sugeno (T-S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use. While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator.

This paper presents a position based visual tracking system of a redundant manipulator using a Kinect camera. Kinect camera provides 3-D information of a target object, therefore the control algorithm of the position-based visual servoing (PBVS) can be simplified, as there is no requirement to estimate a 3-D feature point position from the extracted image and the camera model. The Kalman filter is used to predict the target position and velocity. This control method is applied to a calibrated robotic system with eye-to-hand configuration. The stability analysis has been derived and real-time experiments have been carried out using a 7 DOF PowerCube manipulator from Amtec Robotic. The experimental results of both static and moving targets are presented to demonstrate and to verify the proposed position based visual tracking system performance.

Sistem Monitoring Budidaya Ikan Lele Berbasis Internet Of Things Menggunakan Raspberry Pi

This paper presents a Model Based Visual Servoing (MBVS) control strategy for a differential drive mobile robot navigation using single camera attached on the robot platform. Four points image features are used to compute the angular velocities of the right wheel and the left wheel. The model based visual servoing scheme was simulated using visual servoing platform (ViSP) libraries. The simulation results show the effectiveness of the designed control law. The control law performances are measured both in the image space and in the control input space. The effectiveness of the MBVS has also been verified in real-time experiments using Beaglebone Black as the main hardware controller.

This paper presents a distributed fuzzy proportional control system for a vision guided redundant robot manipulator. Firstly, the Takagi Sugeno (TS) fuzzy algorithm is used to model analytical Image Based Visual Servoing (IBVS) using shape moments by offline learning. This control method is applied to an uncalibrated robotic system with eye-in-hand visual feedback. The system is able to track a moving object through a variety of motions and maintain the object's image features in a desired position in the image plane without a priori knowledge of the robot kinematic, camera calibration and inverse Jacobian. The experimental results of both static and moving targets using the 7 DOF PowerCube manipulator from Amtec Robotic show and verify its performance in a realtime application.