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研究生： 盧脩塵
Hsiu-Chen Lu
論文名稱： 基於類神經網路模型之具障礙物雙線驅動連續體機器人位移分析
Displacement Analysis of a Two-Wire-Driven Continuum Robot with a Contacted Obstacle based on Neural Network Model
指導教授： 郭進星
Chin-Hsing KUO
口試委員: 林其禹
Chyi-Yeu Lin
林柏廷
Po-Ting Lin
陳冠辰
Guan-Chen Chen
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 70
中文關鍵詞： 連續體機器人 、連續體機構 、類神經網絡 、接觸之障礙 、位置分析
外文關鍵詞： continuum robot, continuum mechanism, contacted obstacle, displacement analysis, neural network
相關次數： 點閱：285  下載：2 
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    • 連續體機器人是近年來非常熱門的研究領域。為了控制機器人，必須先知道機器人的位置，因此機器人的位置分析在機器人的研究領域中非常重要。在有關於連續體機器人的研究中，有很多研究對於末端有附載的連續體機器人做出完善的運動學分析，也有非常多關於路徑規劃的研究，教導機器人如何繞過障礙物。繞過障礙物是連續體機器人的一個特點，然而在繞過障礙物的同時，機器人很容易與障礙物產生接觸；針對這個問題，目前沒有任何文獻提出運動學分析，因此從接觸障礙物之線驅動連續體機器人之位置分析著手。傳統的運動學模型可粗分為幾何模型及樑理論模型，幾何模型過於簡化因此不考慮，樑理論模型則過於複雜，連續體機器人與障礙物的接觸點在運動的過程中不停的改變，模型必須不斷即時更新資訊才能使用。最後，決定使用類神經網絡模型，並透過多次實驗獲得數據，藉以訓練類神經網絡模型。訓練完成的模型，可迅速得到準確的機器人位置資訊。

    Continuum robot becomes more and more popular in recent years. Displacement analysis is necessary to find the position of continuum robots. In robot control, there are a lot of researches on path planning and how to bypass the obstacle. There are also many methods can be used to analyze the displacement of continuum robots like geometry models and beam theory models. These methods can also solve the kinematics problem about continuum robot with end loads. However, there is no research about what will happen when a continuum robot contacts an obstacle and keep going. Therefore, the purpose of this thesis is analyzing displacement of a continuum robot with a contacted obstacle. The geometry models are too simple, some of the beam theory models are too complicated, and some cannot solve this problem. Afterward, the neural network model is chosen to analyze displacement of continuum robot with contacted obstacle although there is no research about displacement analysis using a neural network model. By using the neural network model, the user only needs to decide what are the input data, the target data, and then do some experiments to get data. These data would be used to train the neural network model. After that, a trained model would be produced. The trained neural network model will output the position and orientation of the end-effector of the robot in a short time.

    摘 要 1 Abstract 2 致 謝 3 Table of Contents 4 List of Tables 6 List of Figures 7 Chapter 1 Introduction 9 1.1 Motivations and Objectives 9 1.2 Literature Review 11 1.2.1. Geometry Models 12 1.2.2. Beam Theory Models 12 1.2.3. Neural Network Model 13 1.3 Contributions 13 1.4 Thesis Organization 14 Chapter 2 Neural Network Model 16 2.1 Principle of Neural Network 16 2.2 Types of Neural Network 19 2.3 Algorithms of Neural Network 20 2.4 Advantage and Disadvantage of Neural Network Model 21 2.5 Summary 21 Chapter 3 Displacement Analysis for a Large-Deflection Beam with a Contacted Obstacle 23 3.1 Problem Description 23 3.2 Data Collection from ANSYS Simulation 24 3.3 Training by Neural Network Model 26 3.4 Numerical Example 28 3.5 Results and Discussions 29 3.6 Summary 29 Chapter 4 Displacement Analysis of a Two-Wire-Driven Continuum Robot with a Contacted Obstacle 30 4.1 Problem Description 30 4.2 Data collection from Experimental Study 32 4.2.1. Experimental Setup 32 4.2.2. Image Processing 33 4.2.3. Data Collection 38 4.3 Training by Neural Network Model 41 4.4 Numerical Example 48 4.5 Results and Discussions 53 4.6 Summary 54 Chapter 5 Conclusions and Future Works 55 5.1 Conclusions 55 5.2 Future Works 56 References 57 Appendix: Execution of Neural Network Models on MATLAB 63

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