利用神经网络对乒乓球机器人的击球过程进行建模

Kun Zhang, Zaojun Fang, Jianran Liu, M. Tan
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引用次数: 5

摘要

如何将来球击回目标位置,是乒乓球机器人准确获取球拍参数的关键因素。为了对笔划过程进行建模,建立了一种基于多神经网络的模型。神经网络的输入数据是击球过程中球的速度差,输出数据是球拍的参数。为了减少无效数据的影响,建立了基于每个经验数据的神经网络。训练数据基于经验数据聚类。选择神经网络计算球拍参数的方法取决于新数据与经验数据的比较。此外,还提出了一种基于双目视觉系统的脑卒中模型验证方法。实验结果表明,该方法建立的冲程模型是适用的,验证方法是有效的。
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Modeling the stroke process in table tennis robot using neural network
To hit incoming balls back to a desired position, it is a key factor for table tennis robot to get racket parameters accurately. For modeling the stroke process, a novel model is built based on multiple neural networks. The input data for neural networks are the ball velocity differences during the stroke, and racket parameters are the output data. To reduce the influences from the invalid data, a neural network based on each empirical data is established. The training data are clustered based on the empirical data. The way of choosing a neural network to compute the racket parameters depends on the comparison between the new coming data and the empirical data. Moreover, a novel way based on a binocular vision system to verify the stroke model is proposed. Experimental results have showed that the stroke model created via the proposed method is applicable and the verification method is effective.
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