We investigate the shot-aiming problem in Pinball and design a model predictive controller to take shots toward targets with minimal trajectory error using a k-Nearest Neighbor regression-based flip function. A flip function maps the ball's state to its launch trajectory parameters. The k-Nearest Neighbor regression algorithm is applied to reduce the number of calibration shots required to estimate the flip function accurately. The controller is developed for a simulator built with Visual Pinball. The test results from this controller are compared to a controller built with a polynomial flip function. The experiments showed that the controller could accurately aim shots toward targets with minor trajectory errors after a few initialization shots.


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    Title :

    Real-Time Model Predictive Control for Shot Aiming in Pinball Using k-NN Regression


    Contributors:


    Publication date :

    2023-08-28


    Size :

    1725927 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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