Research on natural language processing, such as for image and speech recognition, is rapidly changing focus from statistical methods to neural networks. In this study, we introduce speech recognition capabilities along with computer vision to allow a robot to play snooker completely by itself. The color of the ball to be pocketed is provided as an audio input using an audio device such as a microphone. The system is able to recognize the color from the input using a trained deep learning network. The system then commands the camera to locate the ball of the identified color on a snooker table by using computer vision. To pocket the target ball, the system then predicts the best shot using an algorithm. This activity can be executed accurately based on the efficiency of the trained deep learning model.
|Commitee:||Marayong, Panadda, Torabzadeh, Jalal|
|School:||California State University, Long Beach|
|Department:||Mechanical and Aerospace Engineering|
|School Location:||United States -- California|
|Source:||MAI 58/03M(E), Masters Abstracts International|
|Subjects:||Mechanical engineering, Robotics, Artificial intelligence|
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