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研究生:李文恩
研究生(外文):Wan-An Lee
論文名稱:行動式機械手臂之最小移動基因演算最佳化組態及運動控制
論文名稱(外文):GA-Based Optimal Configuration and Kinematics Control of a Mobile Manipulator with Minimal Movement
指導教授:蔡清池
指導教授(外文):Ching-Chih Tsai
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:73
中文關鍵詞:行動式機械手臂最坐化組態最小移動
外文關鍵詞:mobile manipulatoroptimal configurationminimal movement
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本論文旨在發展一行動式機械手臂系統之任務規劃的設計方法與實現技巧。本文提出一個以基因演算法則為基礎的任務規劃方法來搜尋行動式機械手臂系統之最佳化的組態。為了達到移動車體的點對點位置調整控制,提出三個非線性運動學控制法則並透過Lyapunov 分析方法確保其穩定性。手臂動作之路徑規劃是由一個簡單的二階方程式來完成。實驗用之行動式機械手臂系統是由一個可移動式車體系統、一個機械式手臂系統和一台主核心電腦控制系統所組成。搭配德州儀器公司生產之DSP晶片所設計出來的控制器被用來達成移動式機械手臂系統之精確的速度/位置控制。透過電腦模擬以及實驗結果驗證了本論文所提出之控制理論具有可行性及有效性。
This thesis develops methodologies and techniques for design and implement of a task planning for a mobile manipulator system. A GA-based task planning method is developed to search the near optimal configurations of the mobile manipulator. In order to achieve the point-to-point regulation control of the mobile vehicle, three nonlinear kinematic control laws are designed to ensure the stability via Lyapunov method. A simple path planning for the motion of manipulator is regulated by a second order equation. The experimental system is composed of a mobile vehicle subsystem, a manipulator subsystem and a host computer. The DSP-based proportional-integral speed/position controllers are designed to achieve an accurate speed/position control of the mobile manipulator. Computer simulations and experimental results are included to illustrate the feasibility and effectiveness of the proposed control laws.
Contents
Chinese Abstract i
English Abstract ii
Acknowledgments iii
Contents iv
List of Figures vii
List of Tables x
Nomenclature xi
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Survey of Related Research 3
1.3 Contributions of the Thesis 5
1.4 Organization of the Thesis 6
Chapter 2 System Configuration and Control Architecture 8
2.1 Introduction 8
2.2 Description of Vehicle Motion Control System 8
2.2.1 DSP-based Motion Controller 11
2.2.2 DC Motor Driving 14
2.2.3 Development of Odometer 15
2.3 Description of Manipulator Control System 17
2.3.1 The Robot Arm 18
2.3.2 The Motor Kit 20
2.3.3 DSP-based Motion Controller Development for Manipulator of Three
Degrees of Freedom 22
2.4 Concluding Remarks 25
Chapter 3 GA-based Optimal Configuration for a Mobile Manipulator Using Minimum Movement 26
3.1 Introduction 26
3.2 Task Planning for Minimal Movement 27
3.3 GA-based Point-to-Point Task Planning 31
3.4 Computer Simulation 35
3.6 Concluding Remarks 35
Chapter 4 Point-to-Point Configuration Control 37
4.1 Introduction 37
4.2 Point-to-Point Control of a Mobile Vehicle 38
4.2.1 Regulation Control Law I 38
4.2.2 Computer Simulation of Control Law I 40
4.2.3 Regulation Control Law II 45
4.2.4 Computer Simulation and Experiment of Control Law II 51
4.2.5 Regulation Control Law III 57
4.2.6 Computer Simulation and Experiment of Control Law III 60
4.3 Point-to-Point Control of the Three-link Manipulator 66
4.4 Concluding Remarks 70
Chapter 5 Summaries and Recommendations 72
5.1 Summaries 72
5.2 Recommendations 73
References 74
List of Figures
Figure 2.1 Physical configuration of the mobile vehicle system 9
Figure 2.2 Block diagram of the motion control system 10
Figure 2.3 A photograph of mobile vehicle 11
Figure 2.4 Physical view of the DSP-based control board 13
Figure 2.5 Step response of the motion controller 13
Figure 2.6 H type driving for DC servo motor 14
Figure 2.7 A photograph of the odometer 16
Figure 2.8 Mobile Manipulator Axes 19
Figure 2.9 Physical view of the SCORBOT-ER VII 19
Figure 2.10 The photo of motor kit 21
Figure 2.11 The flowchart of manipulator control system 23
Figure 2.12 Photos of the DSP-based controller for ARM 24
Figure 3.1 Structure of the mobile manipulator system 30
Figure 3.2 The program flowchart of GA 34
Figure 4.1 Simulation block diagram of control law I 40
Figure 4.2 Time response of linear velocity in control law I 42
Figure 4.3 Detailed time response of linear velocity in the beginning 42
Figure 4.4 Time response of angular velocity in control law I 43
Figure 4.5 Detailed time response of angular velocity in the beginning 43
Figure 4.6 Convergent behavior of error signals 44
Figure 4.7 Simulated trajectory of mobile vehicle 44
Figure 4.8 Trajectory near the desired position 45
Figure 4.9 Vehicle’s position and orientation with respect to the target frame 47
Figure 4.10 Simulation block diagram for controller II 51
Figure 4.11 Time response of linear velocity in control law II 53
Figure 4.12 Time response of angular velocity in control law II 53
Figure 4.13 Trajectory of mobile vehicle in X-Y plane 54
Figure 4.14 The behavior of mobile vehicle in the beginning and in the end 55
Figure 4.15 Convergent behavior of error signals 56
Figure 4.16 Motion trajectory of experiment result of control law III 57
Figure 4.17 Simulation block diagram for controller III 60
Figure 4.18 Time response of linear velocity in control law III 62
Figure 4.19 Time response of angular velocity in control law III 62
Figure 4.20 Trajectory of mobile vehicle in X-Y plane 63
Figure 4.21 The behavior of mobile vehicle in the beginning and in the end 64
Figure 4.22 Convergent behavior of error signals 65
Figure 4.23 Motion trajectory of experiment result of control law III 66
Figure 4.24 A manipulator with three degrees of freedom 68
Figure 4.25 The experimental trajectory of the path planning 69
Figure 4.26 The difference of the trajectory between the experiment and simulation 69
List of Tables
Table 2.1 Robot Arm Specifications 20
References
[1] W. Carriker, P. Khosla, and B. Krogh, “An approach for coordinating mobility and manipulation,” Proceedings of IEEE International Conference on System Engineering, pp. 59-63, August 1989.
[2] W. Carriker, P. Khosla, and B. Krogh, “The use of simulated annealing to solve the mobile manipulator path planning problem,” Proceedings of IEEE International Conference on Robotics and Automation , pp. 13-18 , May 1990.
[3] M. Zhao, N. Ansari and E. Hou, “Mobile Manipulator Path Planning By A Genetic algorithm,” Proceedings of lEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 681-688 , July 1992.
[4] J.-G. Kang and J.-M. Lee, “A study on optimal configuration for the mobile manipulator considering the minimal movement,” Proceedings of IEEE International Symposium on Industrial Electronics, pp. 546-551, Dec. 2000.
[5] N. Ansari, M. Chen, and E.-S. Hou, “Point patter matching by a genetic algorithm,” Proc. IECON 90, 16th Conf. of IEEE Industrial Electronics Society, Pacific Grove, CA, Nov. pp. 1233-1238. Nov. 1990.
[6] D.-K. Chwa, J.-H. Seo, P. Kim and J.- Young Choi, “Sliding mode tracking control of nonholonomic wheeled mobile robots,” Proceedings of American Control Conference, 2002. pp. 3991-3996. May 2002.
[7] M. Aicardi, G. Casalino, A. Bicchi and A. Balestrino, “Closed loop steering of unicycle like vehicles via Lyapunov techniques,” Robotics & Automation Magazine, IEEE. pp. 27 — 35. March 1995.
[8] Dixon, Dawson, Zergeroglu, and Behal, Nonlinear Control of Wheeled Mobile Robots, Springer. pp. 2-10. 2001.
[9] R.-P. Paul, Robot Manipulators: Mathematics, Programming, and Control, The Massachusetts Institute of Technology. 1981.
[10] C.-T. Lin and C. S. George Lee, Neural Fuzzy Systems-A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall International, Inc.,1996
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