Neural network-based state prediction for strategy planning of an air hockey robot

被引:2
|
作者
Park, JI
Partridge, CB
Spong, MW
机构
[1] Yeungnam Univ, Sch EE Engn, Kyongsan 712749, South Korea
[2] Univ Illinois, Coordinated Sci Lab, Urbana, IL 61801 USA
来源
JOURNAL OF ROBOTIC SYSTEMS | 2001年 / 18卷 / 04期
关键词
Data acquisition - Functions - Mathematical models - Neural networks - Robotics - State estimation - Strategic planning;
D O I
10.1002/rob.1015
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
We analyze a neural network implementation for puck state prediction in robotic air hockey. Unlike previous prediction schemes which used simple dynamic models and continuously updated an intercept state estimate, the neural network predictor uses a complex function, computed with data acquired from various puck trajectories, and makes a single, timely estimate of the final intercept state. Theoretically, the network can account for the complete dynamics of the table if all important state parameters are included as inputs, an accurate data training set of trajectories is used, and the network has an adequate number of internal nodes. To develop our neural networks, we acquired data from 1500 no-bounce and 1500 one-bounce puck trajectories, noting only translational state information. Analysis showed that performance of neural networks designed to predict the results of no-bounce trajectories was better than the performance of neural networks designed for one-bounce trajectories. Since our neural network input parameters did not include rotational puck estimates and recent work shows the importance of spin in impact analysis, we infer that adding a spin input to the neural network will increase the effectiveness of state estimates for the one-bounce case. (C) 2001 John Wiley & Sons, Inc.
引用
收藏
页码:187 / 196
页数:10
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