Efficient hand eye calibration method for a delta robot pick-and-place system

Delta Robot is a kind of parallel robot manipulator specifically designed for the pick-and-place task. Most of such system requires vision to detect the randomly placed workpieces. Therefore, hand eye calibration is a necessary process when installing new product line or modifying the existing robotic configuration. Because the vision and robot do not share the same workspace, and the workpiece move continuously on the conveyor, it is difficult to perform the hand eye calibration efficiently and directly. In this paper, we propose a systematic framework to efficiently calibrate the vision, conveyor and the robot. Beyond two commonly used chess board based calibration board, no external equipments are needed. A virtual coordinate frame which shares the same origin with the vision coordinate frame is imposed on the conveyor. Based on this assumption, two independent experiments can be performed to gather data sets which can be used to optimize the coordinate transformation matrices. Linear Mean Square(LMS) algorithm is used to find the optimal parameters. A self feed high speed pick and place experimental platform is built and the proposed algorithm is implemented. The experimental results show the effectiveness and efficiency. All the system can be calibrated within 10 minutes.