Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Bachelor Program in Interdisciplinary Studies, College of Future, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Sensors (Basel). 2020 Dec 17;20(24):7233. doi: 10.3390/s20247233.
In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO ("you only look once") to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.
近年来，芯片设计技术和人工智能（AI）都取得了重大进展。这迫使所有领域都在研究如何利用机器学习技术来提高产品竞争力。在这项工作中，我们主要使用深度学习结合电机控制来实现实时互动的空气曲棍球系统，并验证机器学习在实时互动系统中的可行性。特别是，我们使用卷积神经网络 YOLO（“你只看一次”）来捕捉冰球当前位置。同时，应用反射定律和神经网络来预测冰球的最终位置。基于预测位置，系统将控制步进电机移动线性滑台，从而实现实时互动的空气曲棍球系统。最后，我们讨论并验证了冰球最终位置预测的准确性，并提高了系统响应时间，以满足系统要求。
