VISION BASED MOTION CONTROL AND TRAJECTORY TRACKING FOR MICROASSEMBLY ROBOTS

Abstract

This paper presents a vision based motion control and trajectory tracking strategies for microassembly robots including a self-optimizing visual servoing depth motion control method and a novel trajectory snake tracking strategy. To measure micromanipulator depth motion, a normalized gray-variance focus measure operator is developed using depth from focus techniques. The extracted defocus features are theoretically distributed with one peak point which can be applied to locate the microscopic focal depth via self-optimizing control. Tracking differentiators are developed to suppress noises and track the features and their differential values without oscillation. Based on the differential defocus signals a coarse-to-fine self-optimizing controller is presented for micromanipulator to precisely locate focus depth. As well as a novel trajectory snake energy function of robotic motion is defined involving kinematic energy, curve potential and image potential energy. The motion trajectory can be located through searching the converged energy distribution of the snake function. Energy weights in the function are real-time adjusted to avoid local minima during convergence. To improve snake searching efficiency, quadratic-trajectory least square estimator is employed to predict manipulator motion position before tracking. Experimental results in a microassembly robotic system demonstrate that the proposed strategies are successful and effective.