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In this paper, the control problem of camera-in-hand robotic systems is considered. In this approach, a camera is mounted on the robot, usually at the hand, which provides an image of objects located in the robot environment. The aim of this approach is to move the robot arm in such a way that the image of the objects attains the desired locations. We propose a simple image-based direct visual servo controller which requires knowledge of the objects' depths, but it does not need to use the inverse kinematics and the inverse Jacobian matrix. By invoking the Lyapunov direct method, we show that the overall closed-loop system is stable and, under mild conditions on the Jacobian, local asymptotic stability is guaranteed. Experiments with a two degrees-of-freedom direct-drive manipulator are presented to illustrate the controller's performance.

Background: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well.

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Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

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