Intelligent system for intercepting moving objects

Kawempy, Ivan; How, Khoo Boon

doi:10.1109/raics.2011.6069418

Cited by 2 publications

(1 citation statement)

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“…The mutual conversion of the color space is used to eliminate the influence of the brightness on the color characteristics of the puck [ 2 ]. The prediction first assumes that the puck moves in a straight line, and a linear formula is used as the prediction method after the last impact on the edge of the table or after the puck enters the specified area [ 3 ]. It was also proposed to use a neural network for the end point.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Air Hockey Interactive Control System

Chang

Hsu

Chang

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%