Visual servoing control in a cluttered environment based on artificial intelligence.

The necessity of designing flexible and versatile systems is one of the most current trends in robotic research. Including visual servoing techniques in an existing robotic system is a very challenging task. In this project a solution for extending the capabilities of a 6 DOF manipulator robot for visual servoing system development, is proposed. In order to achieve this task, different types of visual features (which can be extracted from the image using a visual sensor) are detected and their properties are analyzed. Here, visual features such as point features and image moments are taken into account for designing the controller. An image-based control architecture is designed and a real-time implementation on a manipulator robot is developed. The primary objective of this research project is to converge into an accurate algorithm for object reconstruction in a clutter environment and subsequently helping the robot to perform a visual servoing task. The object reconstruction is done by employing tools from artificial intelligence such as deep Convolutional Neural Network. The image acquisition and image processing together with the computing of the image-based control law will be implemented in Matlab. Thus, a new type of robot driving interface that links the robots' controller with Matlab environment is proposed. Such a user driver interface will allow not only to design and implement real-time controllers but also to perform other tasks such as identification, path planning, etc. Finally, the robustness and stability of the proposed visual feature based control law will be implemented, tested and validated in real-time through multiple experiments.

Keywords:MANIPULATOR ROBOT, VISUAL SERVOING, IMAGE FEATURES

Disciplines:Robot manipulation and interfaces, Automation, feedback control and robotics