Spin observation and trajectory prediction of a ping-pong ball
Spin observation and trajectory prediction of a ping-pong ball
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DOI：
10.1109/icra.2014.6907456
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Yifeng Zhang;Yongsheng Zhao;R. Xiong;Yue Wang;J. Wang;Jian Chu
中科院分区：
文献类型：
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作者：
Yifeng Zhang;Yongsheng Zhao;R. Xiong;Yue Wang;J. Wang;Jian Chu
对于乒乓球机器人来说，实时观察球并准确预测球的轨迹是必不可少的。然而，现有的视觉系统大多只能提供球的位置观察，而没有考虑到球的旋转，这是非常重要的比赛。本文提出了一种实时观测和估计球的旋转的方法，并实现了准确的预测。基于旋转球的运动可以分为整体运动和绕中心旋转两种情况，我们构造了一个一体化的视觉系统来分别观察这两种运动。利用云台视觉系统，通过识别球上商标的位置，恢复球的三维姿态，观察旋转运动。然后用平面拟合的方法对当前和历史观测资料进行了自旋状态估计。同时利用位置和尾旋信息，通过扩展卡尔曼滤波器（EKF）实现了精确的状态估计和轨迹预测。实验结果表明了该方法的有效性和准确性。
For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.