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

We analyze a neural network implementation for puck state prediction in rob otic air hockey. Unlike previous prediction schemes which used simple dynam ic 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 fina l intercept state. Theoretically, the network can account for the complete dynamics of the table if all important state parameters are included as inp uts, 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 performa nce of neural networks designed to predict the results of no-bounce traject ories was better than the performance of neural networks designed for one-b ounce trajectories. Since our neural network input parameters did not inclu de rotational puck estimates and recent work shows the importance of spin i n 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.