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Modeling the stroke process in table tennis robot using neural network
Zhang K(张鵾); Fang Zaojun; Liu Jianran; Tan Min
2016-02-25
Conference NameIEEE International Conference on Robotics and Biomimetics
Pages243-248
Conference Date2015
Conference PlaceZhuhai, China
AbstractTo 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.
KeywordNeural Networks Trajectory Cameras Robot Kinematics Machine Vision Data Models
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21171
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation中国科学院自动化所
Recommended Citation
GB/T 7714
Zhang K,Fang Zaojun,Liu Jianran,et al. Modeling the stroke process in table tennis robot using neural network[C],2016:243-248.
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