Visual servoing of 4DOF using image moments and neural network
基于图像矩与神经网络的机器人四自由度视觉伺服

To avoid the complicated marking, extracting and matching of image features in the traditional visual servoing systems and to improve the universality of the algorithm, a novel visual servoing of 4-degrees of freedom(4DOF) is proposed for an eye-in-hand robot based on image moments and neural network. First, the nonlinear transform relationship between image moments and the robot pose is developed, which provides the theoretical basis for the visual servoing using image moments. Then, a back propagation(BP) neural network is designed to map the transformation from image moments variation to the robot pose displacement with 4DOF without the external and internal parameters calibration for the camera. After this, the proposed control scheme can be applied to the robotic visual servoing. The experiment results show that the tracking error is less than 0.5 mm and 0.5°respectively in position and in orientation. This confirms the validity and satisfactory servoing performance of the proposed method.