人工智慧於機器手臂插銷控制應用

Abstract

隨著機器人的應用日益廣泛，生產線上動態取放的應用也逐漸增加，透過機器人視覺應用以及引入人工智慧，可以大幅降低傳統機械手臂所需要的輔助人力。本論文主要研究目的為機械手臂插銷控制應用，結合機器人學以及人工學習的機器人系統達成兩個任務：隨機取放、模擬人類手腕調整歪斜螺絲。 本文首先，實現在不同尺寸、角度以及明暗度的模板比對演算法，並設計簡易整合介面，達成隨機取放。並引入強化學習，結合遠端運動中心控制，教導機器人類比人類手腕轉動調整螺絲歪斜狀況。

Owing to the applications of robot arm become increasingly extensive, dynamic pick-and-place on the production line has gradually increased. Through robot vision system and artificial intelligence , the requirements of operation personnel can be greatly reduced. The purpose of this thesis is the robot arm plugging control application. Combining robotics and artificial intelligence into robot systems, two tasks are achieved: random pick and place, and simulation of human wrist adjustment skew screws. In this paper, we first implement the rotation, scale, translation-invariant template matching algorithm , and design a simple integration interface to achieve random pick and place. Using reinforcement learning technique, combined with remote center of motion control, training the robot to learn how to adjust the skew of the screw like the human wrist did.