Conference Paper

A robotic ball catcher with embedded visual servo processor

Dept. of Electr. & Control Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
DOI: 10.1109/IROS.2010.5648912 Conference: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Source: IEEE Xplore


In this work we present a robotic ball catcher with embedded visual servo processor. The embedded visual servo processor with powerful parallel computing capability is used as the computation platform to track and triangulate a flying ball's position in 3D based on stereo vision. A recursive least squares algorithm for model-based path prediction of the flying ball is used to determine the catch time and position. Experimental results for real time catching of a flying ball are presented by a 6-DOF robot arm. The percentage of success rate of the robotic ball catcher was found to be approximately 60% for the ball thrown to it from five meters away.

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    ABSTRACT: This work proposes a technique for calibration of an eye-to-hand system. The target of the hand-eye calibration is to estimate the geometric transformation between the hand and the eye. This calibration method further considers camera intrinsic parameters and geometric relations of a working plane in space at the same time. A laser pointer casually mounted on the hand is utilized. By manipulating the robot and projecting the laser beam on a plane of unknown orientations, a batch of related image positions of light-spots are extracted from images of the camera. Since the laser is rigidly mounted and the plane is fixed at each orientation, the geometric parameters and measurement data must obey a certain nonlinear constraints and the solutions of parameters can be estimated accordingly. A close-form solution is developed by decoupling the nonlinear equations into linear forms to compute all of the initial values. As a result, the calibration method does not need any manual initial guess of the unknown parameters. To achieve a higher accuracy, a nonlinear optimization method is implemented to refine the estimation. The advantage of using laser pointer is that this technique can be used for the case when the eye does not see the hand. Experimental results of simulations and real data are presented to show the validity and the simple requirements of the proposed algorithm.
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