Abstract
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.
Highlights
Advances in integrated circuit (IC) design technology have made significant contributions in both memory space and processing power
The stepping motor has a motor has a stator and rotor that are like gear-like protrusions and fit each other, and the great self-retaining forcetowhen it generates a magnetic field after energized, can maintain current flowing the stator coil is switched to gradually rotatebeing at a certain angle. so Theitstepping motor the stopped position even without using a mechanical brake
YOLOv3, the accuracy and execution time of the hockey end position prediction of the different movement control in method 2, the fastest ball speed that the system can block after the actual test, methods, the frequency pulse combination of different modes of stepping motors, the defender’s and 100 actual game results
Summary
Advances in integrated circuit (IC) design technology have made significant contributions in both memory space and processing power. YOLO is an algorithm that uses target detection as a regression problem It gets features by a convolutional neural network, and gets bounding boxes and confidence score by a fully connected layer, split image into S × S grid, and predicts bounding boxes in every grid. A camera is set up on the air hockey table, the real-time image is captured and connected to a personal computer (PC) via a USB interface, and the position of the hockey puck in the image is identified using the YOLOv3 convolutional neural network. It uses the law of reflection and neural network to predict the possible final point of the hockey.
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