Design of the neural-fuzzy compensator for a billiard robot

Abstract

A billiard robot is design to imitate the learning ability of human beings to play billiards. The objective of this research is to design a neural-fuzzy compensator for this billiard robot to improve the billiards skill. First, the predictable hitting error model is developed based on the recorded database of pocketing processes. Then, the predictable error is compensated by the fuzzy controller to decide the cutting angle (hitting point) of the object ball automatically. We confirm the sufficient accuracy to sink the ball into the designated pocket by experiments and numerical analysis.