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。
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