Grey decision-making for a billiard robot

Billiard is one of the most complex games to play in the real world. A player needs to visualize the situation between balls and pockets and to score the ball into the designate pocket by his/her own experience. A billiard robot is developed to imitate the behavior of human beings to play billiard. There are machine vision, decision-making, control and actuating subsystems in the experiment setup. The objective of this paper is to design a decision algorithm for a billiard robot by using grey theory. The results indicate that the decision algorithm work very well in both the simulation and experiment.