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    Title: 灰色系統理論在撞球機器人之清檯攻擊研究
    Other Titles: The study of clean-table offensive techniques for a billiard robot by the grey system theory
    Authors: 劉苡宗;Liu, Yi-Zong
    Contributors: 淡江大學機械與機電工程學系碩士班
    楊智旭;Yang, Jr-Syu
    Keywords: 撞球機器人;灰色理論;碰撞理論;Billiard robot;grey theory;collision theory
    Date: 2011
    Issue Date: 2011-12-28 19:13:56 (UTC+8)
    Abstract: 本研究主要目的是針對撞球機器人在九號球球局中,利用碰撞理論規劃母球在四種攻擊技巧下(直線攻擊、顆星攻擊、灌球攻擊、組合球攻擊),以及在每種攻擊的五種不同力道撞擊後的反彈路徑,利用灰決策針對擊球方式以及擊球力道的不同,找出母球的各種移動路徑以及停留位置來決定最適合打擊下顆目標球的攻擊方式以及擊球力道,達到作球的目的。最後以能夠連續得分順利清檯,贏得球局為目標。
    首先由攝影機擷取影像,找出各球之顏色及球心,再以所撰寫之VB程式透過本研究之碰撞基礎理論規劃出所有的攻擊方式,以及攻擊力道打擊後母球移動的路徑。根據過去的實驗結果所累積的經驗,在直線攻擊的進攻下進球率最高,因此,利用灰決策來針對所有路徑的終點位置,採用在下顆目標球的進攻路徑上無障礙球存在,以及攻擊角度越小、球與球袋距離越小等衡量條件來決定最佳的攻擊方案。此外,除了將母球設計在適合打擊下顆目標球的位置,還得確保首次進攻必須順利得分,否則之後的作球便無意義,因此本研究設計在打擊前的模擬規劃必須先排除攻擊路徑上有障礙球干擾,以及進球率低於60%的攻擊方式,最後從這些符合條件限制的攻擊方案中選出測度值最高者為最佳攻擊方案,並達到連續得分,順利清檯贏得球局的目的。
    The objective of this thesis is to make a position play for a billiard robot in a nine ball pool game by the Grey 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.
    In the research. The color and center positions are decided by the developed VB software from the captured image. 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 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 work very well in the pool game.
    Appears in Collections:[機械與機電工程學系暨研究所] 學位論文

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