臺灣博碩士論文加值系統

目前在工業上拋光、去毛邊、焊接及插削等項目主要都是由串聯式機械手臂來完成，但是並聯式機械手臂相對於串聯式機械手臂來說，它有更佳的負重能力、低慣性，及高速運動能力，並且機械結構簡單卻不易變形等優勢。然而主流的並聯式機械手臂Delta robot只有三自由度，只能朝xyz方向移動，因此Delta robot在產業界上的大部分應用都只能集中在食物封裝、零件整列等用途上，對於需要高自由度的工作項目，產業界仍偏向採用串聯式機械手臂。本研究首先探討六自由度並聯式機械手臂(Hexa robot)，並結合Hogan的阻抗控制理論，在基於位置控制的模式下，去設計整個手臂的控制系統，藉由架設在手臂終端器的力量感測器量測到的力量感測值，經阻抗控制法則，得到位置修正量，以此調整機械手臂的姿態，達到順應環境的能力，避免手臂在工作過程中，與工件有不當的碰撞，造成機械手臂或工件的損壞。本研究將六自由度並聯式機械手臂從傳統的位置控制模式下延升到能順應環境的阻抗控制，透過對傳統的機械手臂控制器的位置控制模式架構下發展出阻抗控制，使傳統機械手臂控制器在位置控制下也能完成阻抗控制之功能並且探討不同環境參數，會使阻抗控制器產生何種影響，以此完成阻抗實驗的參數調整。
本研究針對六自由度並聯式機械手臂透過向量法推導其逆向運動學，並利用倒傳遞類神經網路去擬合對於並聯式機械手臂來說，求解困難的正運動學，亦推導六自由度並聯式機械手臂的賈氏矩陣(Jacobian matrix)以求出正逆向奇異點，避免機械手臂在運動過程中，發生失去控制之情況。在Simulink環境下模擬六自由度並聯式機械手臂的阻抗控制，藉由調整環境參數來分析環境的變化對阻抗參數的影響，以確定在阻抗實驗時，如何調整參數使控制器穩定。在硬體架構方面搭建六自由度並聯式機械手臂的六軸運動控制系統，並搭配C#程式語言開發的電腦端的人機介面，讓使用者在電腦端能夠控制六自由度並聯式機械手臂，最後完成在C#的編譯環境下搭建出六自由度並聯式機械手臂的位置控制及阻抗控制的運動架構，並分析探討六自由度並聯式機械手臂應用阻抗控制於環境下的效能

At present, polishing, deburring, welding and bolting are mainly done by series mechanical arms in industrial, but the parallel-type robotic arm has advantages such as better load-bearing capacity, low inertia, high-speed movement ability, simple mechanical structure, and non-deformation, as compared with the serial-type robotic arm. However, the mainstream parallel robot (Delta robot) has only three degrees of freedom and can only move in the x-y-z direction. Therefore, most of the applications of Delta robot in the industry can only focus on food packaging, parts alignment. For work projects that require a high degree of freedom, the industry still prefers to use a series robot. This study first explores a six-degrees-of-freedom parallel robotic arm (Hexa robot) and combines Hogan's impedance control theory to design the entire arm control system in a position-based control mode, with a sensor of force built into the arm terminator.The force sensing value measured by the force sensor installed on the arm terminator is used to obtain the position correction amount through the impedance control law, adjust the posture of the robotic arm so as to achieve the ability to adapt to the environment, avoid improper collision of the arm with the workpiece during operation, resulting in damage to the robot arm or workpiece. In this study, the 6-DOF parallel manipulator is extended from the traditional position control mode to the impedance control that can adapt to the environment,through the development of impedance control under the position control mode of the traditional robotic arm controller,the traditional robotic arm controller can also complete the function of impedance control under the position control and discuss different environmental parameters, which will make the impedance controller produce what kind of influence, so as to complete the parameter adjustment of the impedance experiment.
This study derived the inverse kinematics of the 6-DOF parallel robotic arm through the vector method, and use neural network backpropagation to fit forward kinematics for Hexa robot. The Jacobian matrix of the six-degree-of-freedom parallel manipulator is also derived to find the forward and reverse singular points to avoid the loss of control of the robotic arm during the movement.Simulate the impedance control of a six-degree-of-freedom parallel manipulator in a Simulink environment, by adjusting the environmental parameters, analyze the influence of environmental changes on the impedance parameters to determine how to adjust the parameters to stabilize the controller during the impedance experiment. In the hardware architecture, a six-axis motion control system for a six-degrees-of-freedom parallel manipulator is built, and a computer-side of human-machine interface developed with the C# programming language is used. Allows the user to control the 6-DOF parallel robotic arm at the computer, and finally completes the motion control of the position control and impedance control of the 6-DOF parallel robotic arm in the C# compilation environment. Analyze the effectiveness of the six-degrees-of-freedom parallel manipulator applying impedance control to the environment.

摘要 I
Abstract III
致謝 V
目錄 VI
圖目錄 IX
表目錄 XII
第1章 緒論 1
1.1 研究背景 1
1.2 文獻回顧 2
1.2.1 並聯式機械手臂 2
1.2.2 阻抗控制 4
1.3 研究動機 5
1.4 論文架構 6
第2章 Hexa Robot運動學與阻抗控制 7
2.1 Hexa robot 7
2.2 逆向運動學 8
2.3 正向運動學 11
2.4 奇異點分析 12
2.5 工作空間分析 16
2.6 基於倒傳遞類神經之正向運動學 18
2.7 路徑規劃 20
2.8 基於位置之阻抗控制 21
第3章 硬體架構與實驗流程 27
3.1 硬體架構說明 27
3.1.1 Hexa robot平台介紹 27
3.1.2 控制器規格 29
3.1.3 資料擷取系統規格 32
3.1.4 馬達及編碼器規格 33
3.1.5 力量感測器 35
3.2 正運動學類神經網路設計 36
3.2.1 工作空間分析 36
3.2.2 類神經訓練 38
3.2.3 模型結果 39
3.3 系統識別及控制器設計 43
3.3.1 馬達灰箱系統模型 44
3.3.2 系統鑑別實驗 45
3.3.3 PD控制器設計 54
3.4 實驗架構 59
3.4.1 系統模擬架構 60
3.4.2 系統實驗架構 61
第4章 模擬與實驗 66
4.1 阻抗步階模擬分析 66
4.1.1 阻抗控制參數分析 66
4.1.2 環境對於阻抗控制器之影響 69
4.2 五階多項式軌跡實驗 72
4.3 軌跡重現性實驗 81
4.3.1 圓形軌跡 82
4.3.2 繞原點軌跡 85
4.4 阻抗步階響應實驗 90
4.5 阻抗斜坡實驗 97
4.6 阻抗圓形實驗 102
4.7 實驗結果討論 108
第5章 結論與未來展望 117
5.1 結論 117
5.2 未來展望 118
參考文獻 120

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