The Uncalibrated Microscope Visual Servoing for Micromanipulation Robotic System

MEMS technology exploits the existing microelectronics infrastructure to create complex machines with micron feature sizes. These machines can perform complex functions including communication, actuation and sensing. However, micron sized devices with incompatible processes, different materials, or complex geometries, have to be ‘assembled’. Manual assembly tasks need highly skilled operator to pick and place micro-parts manually by means of microscopes and micro-tweezers. This is a difficult, tedious and time consuming work. Visual feedback is an important approach to improve the control performance of micro manipulators since it mimics the human sense of vision and allows for operating on the noncontact measurement environment. The image jacobian matrix model has been proved to be an effective tool to approach the robotic visual servoing problem theoretically and practically. It directly bridges the visual sensing and the robot motion with linear relations, without knowing the calibration model of the visual sensor such as cameras. However, image jacobian matrix is a dynamic timevarying matrix, which cannot be calibrated by fix robotic or CCD camera parameters, especially for micro-manipulation based on micro vision. So, it is an exigent request for us to estimate parameters of image jacobian matrix on-line. Many papers about image jacobian matrix online estimation have been reported. Clearly, Performance of the online estimation of the image jacobian matrix is the key issue for the quality of the uncalibrated micro-vision manipulation robotic. Unfortunately, the current estimation methods have problems such as estimation-lag, singularity, convergence and its speed. Especially in dynamic circumstances, these problems become more serious. There are other efforts to deal with the online estimation of the image jacobian matrix and the uncalibrated coordination control. Piepmeier et al. present a moving target tracking task based on the quasi-Newton optimization method. In order to compute the control signal, the jacobian of the objective function is estimated on-line with a broyden’s update formula (equivalent to a RLS algorithm). This approach is adaptive, but cannot guarantee the stability of the visual servoing. Furthermore, the cost function using RLS is restricted by prior knowledge for obtaining some performance. To deal with those problems discussed above, we apply an improved broyden's method to estimate the image jacobian matrix. Without prior knowledge, the method employs chebyshev polynomial as a cost function to approximate the best value. Our results show that, when calibration information is unavailable or highly uncertain, chebyshev polynomial