Using GM(1,1) to Clean-Table Offensive Techniques

Abstract

The objective of this thesis is to make a position play for a billiard robot in a nine ball pool game by the Grey System Theory. The position play is the placement of the cue ball on the best position to the next planned shot. The robot is able to decide a shooting mode with a corresponding shooting strength from the developed data base of rebound paths of the cue ball. The rebound paths are calculated and recorded from four shooting modes (free shot, cushion shot, bank shot, kiss shot) with five different shooting strengths by the collision theory in a PC. The continuous position play is called the clean-table in the pool game. The moving path of object ball and cue ball are calculated by the collision theory. The grey decision making is developed to find out the best position of cue ball after shooting for the position play. The decision factors are the block ball, the shooting angle, the distance between the object ball and the pocket, and the distance between the object ball and the cue ball. The first priority of the position play is to choose the corresponding object ball and the rebound path of cue ball without any block ball. Then, the second priority is to choose the higher successful pocketing rate (large than 60%). Finally, the offensive decision is set up to make a position play by the Grey Decision-making Sub-system. The experimental results show this clean-table offensive system works very well in the pool game.