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研究生： 胡杰
Huzaefa, Firhan
論文名稱： 多全向輪無人搬運車於外力估測下之合作搬運控制系統
Cooperative Object Transportation for Multiple Omnidirectional Automated Guided Vehicle with External Force Estimation
指導教授： 劉彥辰
Liu, Yen-Chen
學位類別： 碩士
Master
系所名稱： 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 112
外文關鍵詞： cooperative transportation, multi-AGV system, force distribution, mecanum-wheeled platform
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    • This thesis addresses the problem of cooperative transportation using multiple omnidirectional Automated Guided Vehicle (AGV). To enhance flexibility and application potentials, mecanum-wheeled platform is considered for the proposed multi-AGV system, and cooperative transportation is executed without physical link/gripper to fix the object on the AGVs. The position and number of AGV is adjustable depending on the size and weight of the transported object. The analysis of external force distribution to each AGV during cooperative transportation is presented in this thesis. Furthermore, three the external force estimation methods are presented to handle the effect of transported object inertia to each AGV. Moreover, adaptive sliding mode controller is designed for AGV to cope with dynamic uncertainty during cooperative transportation. Stability of the proposed controller is proven by using Lyapunov Theorem. Finally, numerical simulation and experiment are done to demonstrate the performance and efficiency of the proposed multi-AGV control system.

    List of Figures - iii List of Tables - vi Chapter 1 Introduction - 1 1.1 Background and Motivation - 1 1.1.1 Industrial 4.0 and Smart Factory - 1 1.1.2 Cooperative Transportation using Multiple Robots - 3 1.2 Related Works - 4 1.3 Problem Formulation and Research Contribution - 8 1.4 Thesis Structure - 9 Chapter 2 Theoretical Foundations - 12 2.1 Lyapunov Analysis - 12 2.1.1 Nonlinear System - 12 2.1.2 Stability - 13 2.1.3 Lyapunov Stability for non-Autonomous Systems - 14 2.2 Robust and Adaptive Controller - 16 2.2.1 Sliding Mode Control - 16 2.2.2 Boundary Layer Mehtod - 18 2.2.3 Passivity-Based Adaptive Control - 19 2.3 Radial Basis Neural Network - 20 Chapter 3 Dynamic Modeling of Cooperative Transportation Systems - 24 3.1 Cooperative Transportation System - 24 3.2 Modeling of Transported Object - 27 3.3 AGV Dynamic Modeling - 29 Chapter 4 Controller Design - 36 4.1 Force Distribution and Internal Force Analysis - 37 4.1.1 Force allocation to each AGV - 37 4.1.2 Internal Force Regulation using Gradient Projection Methods - 39 4.2 Robust Adaptive Controller Design - 41 4.2.1 Derivation of Robust Adaptive Controller for AGV - 41 4.2.2 Stability Analysis - 44 4.2.3 Simulation - 46 4.3 Adaptive Control Law for External Force Estimation - 52 4.3.1 Force Distribution and Adaptation Mechanism - 52 4.3.2 Stability Analysis - 56 4.3.3 Simulation - 57 4.4 Force Estimation using Radial Basis Function Neural Network - 60 4.4.1 Derivation of RBF Neural Network - 62 4.4.2 Stability Analysis - 64 4.4.3 Simulation - 65 4.5 Performance with more AGV in the system - 68 Chapter 5 Experimental Results - 75 5.1 Experiment Setup - 75 5.1.1 System Description - 75 5.1.2 Automated Guided Vehicle - 77 5.1.3 Motion Capture System - 79 5.1.4 Robot Operating System (ROS) - 80 5.2 Experiment Result and Discussion - 81 5.2.1 Effect of load on system performance - 81 5.2.2 Experiment using Adaptive External Force Estimation - 84 5.2.3 Experiment using RBFNN External Force Estimation - 88 5.2.4 Comparison between Adaptive and RBFNN Force Estimation Method - 92 5.2.5 Experiment for Different AGV Object Shape - 96 5.2.6 Possible Uncertainty in Experiment - 103 Chapter 6 Conclusion and Outlook - 105 6.1 Conclusion - 105 6.2 Outlook - 107 References - 108

    [1] Carvalho Capital. Industrial revoltuion 4.0. http://www.carvalhocapital.com/4ir/.
    [2] Henning Kagermann, Johannes Helbig, Ariane Hellinger, and Wolfgang Wahlster. Recommendations for implementing the strategic initiative INDUSTRIE 4.0, final report of the Industrie 4.0 Working Group. Forschungsunion, 2013.
    [3] KUKA. Kuka omni-move. http://aimagv.com/images/Products/AGV/Types/Unit-Load/.
    [4] Toni Machado, Tiago Malheiro, S´ergio Monteiro, Wolfram Erlhagen, and Estela Bicho. Multi-constrained joint transportation tasks by teams of autonomous mobile robots using a dynamical systems approach. In Robotics and Automation (ICRA), 2016 IEEE International Conference on, pages 3111–3117. IEEE, 2016.
    [5] S. V. Manko, S. A. K. Diane, A. E. Krivoshatskiy, I. D. Margolin, and E. A.Slepynina. Adaptive control of a multi-robot system for transportation of large-sized objects based on reinforcement learning. In 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pages 923–927, Jan 2018.
    [6] J.J.E. Slotine and W. Li. Applied Nonlinear Control. Prentice Hall, 1991.
    [7] L. Bassi. Industry 4.0: Hope, hype or revolution? In 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pages 1–6, Sep. 2017.
    [8] R. Drath and A. Horch. Industrie 4.0: Hit or hype? [industry forum]. IEEE Industrial Electronics Magazine, 8(2):56–58, June 2014.
    [9] Elvis Hozdi´c. Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies, 7:28–35, 01 2015.
    [10] Pilsung Choe, Jeffrey D. Tew, and Songzhen Tong. Effect of cognitive automation in a material handling system on manufacturing flexibility. International Journal of Production Economics, 170:891 –899, 2015. Decision models for the design, optimization and management of warehousing and material handling systems.
    [11] Bastian Solution. Unit load agv. https://www.bastiansolutions.com/solutions/technology/automated-guided-vehicles/unit-load/.
    [12] A. Gautam and S. Mohan. A review of research in multi-robot systems. In 2012 IEEE 7th International Conference on Industrial and Information Systems (ICIIS), pages 1–5, Aug 2012.
    [13] R. N. Darmanin and M. K. Bugeja. A review on multi-robot systems categorised by application domain. In 2017 25th Mediterranean Conference on Control and Automation (MED), pages 701–706, July 2017.
    [14] Elio Tuci, Muhanad Alkilabi, and Otar Akanyeti. Cooperative object transport in multi-robot systems: A review of the state-of-the-art. Frontier in Robotics and AI, 5, 05 2018.
    [15] Tor A. Johansen and Thor I. Fossen. Control allocation—a survey. Automatica, 49(5):1087 – 1103, 2013.
    [16] Robert G Bonitz and Tien C Hsia. Force decomposition in cooperating manipulators using the theory of metric spaces and generalized inverses. In Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, pages 1521–1527. IEEE, 1994.
    [17] Jae Heon Chung, Byung-Ju Yi, and Whee Kuk Kim. Analysis of internal loading at multiple robotic systems. Journal of mechanical science and technology, 19(8):1554–1567, 2005.
    [18] Sebastian Erhart and Sandra Hirche. Internal force analysis and load distribution for cooperative multi-robot manipulation. IEEE Transactions on Robotics, 31(5):1238–1243, 2015.
    [19] Veer Alakshendra and Shital S Chiddarwar. Adaptive robust control of mecanum-wheeled mobile robot with uncertainties. Nonlinear Dynamics, 87(4):2147–2169, 2017.
    [20] N. Tlale and M. de Villiers. Kinematics and dynamics modelling of a Mecanum wheeled mobile platform. In 2008 15th International Conference on Mechatronics and Machine Vision in Practice, pages 657–662, Dec 2008.
    [21] M De Villiers and NS Tlale. Development of a control model for a four wheel mecanum vehicle. Journal of Dynamic Systems, Measurement, and Control, 134(1):011007, 2012.
    [22] Lih-Chang Lin and Hao-Yin Shih. Modeling and adaptive control of an omnimecanum-wheeled robot. Intelligent Control and Automation, 4(02):166, 2013.
    [23] Hassan K Khalil and Jessy W Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, NJ, 2002.
    [24] Bruno Siciliano Oussama Khatib, editor. Springer Handbook of Robotics. Springer International Publishing, 2016.
    [25] M.W. Spong, S. Hutchinson, and M. Vidyasagar. Robot Modeling and Control. Wiley select coursepack. Wiley, 2005.
    [26] Neha Yadav. An Introduction to Neural Network Methods for Differential Equations. 03 2015.
    [27] Junfei Qiao, Xi Meng, and Wenjing Li. An incremental neuronal-activitybased rbf neural network for nonlinear system modeling. Neurocomputing, 302:1 – 11, 2018.
    [28] Kyung-Lyong Han, Hyosin Kim, and Jin Lee. The sources of position errors of omni-directional mobile robot with mecanum wheel. pages 581–586, 10 2010.
    [29] M De Villiers and Glen Bright. Development of a control model for a four wheel mecanum vehicle. Journal of Dynamic Systems, Measurement, and Control, 134, 01 2012.
    [30] S. A. Schneider and R. H. Cannon. Object impedance control for cooperative manipulation: theory and experimental results. IEEE Transactions on Robotics and Automation, 8(3):383–394, June 1992.
    [31] Ian D. Walker, Robert A. Freeman, and Steven I. Marcus. Analysis of motion and internal loading of objects grasped by multiple cooperating manipulators. The International Journal of Robotics Research, 10(4):396–409, 1991.
    [32] P. Donner, S. Endo, and M. Buss. Physically plausible wrench decomposition for multieffector object manipulation. IEEE Transactions on Robotics, 34(4):1053–1067, Aug 2018.
    [33] A. Zambelli Bais, S. Erhart, L. Zaccarian, and S. Hirche. Dynamic load distribution in cooperative manipulation tasks. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2380–2385, Sep. 2015.
    [34] Z. Li and C. Su. Neural-adaptive control of single-master–multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties. IEEE Transactions on Neural Networks and Learning Systems, 24(9):1400–1413, Sep. 2013.
    [35] Zhijun Li, Shuzhi Sam Ge, and Zhuping Wang. Robust adaptive control of coordinated multiple mobile manipulators. Mechatronics, 18(5):239 – 250, 2008. Special Section on Optimized System Performances Through Balanced Control Strategies.
    [36] Bruno Siciliano. Kinematic control of redundant robot manipulators: A tutorial. Journal of Intelligent and Robotic Systems, 3(3):201–212, Sep 1990.
    [37] Yen-Chen Liu and Nikhil Chopra. Control of semi-autonomous teleoperation system with time delays. Automatica, 49(6):1553 – 1565, 2013.
    [38] J-JE Slotine and JA Coetsee. Adaptive sliding controller synthesis for nonlinear systems. International Journal of Control, 43(6):1631–1651, 1986.
    [39] Shafiqul Islam and Xiaoping P Liu. Robust sliding mode control for robot manipulators. IEEE Transactions on Industrial Electronics, 58(6):2444–2453, 2011.
    [40] S.S. Rao. Engineering Optimization: Theory and Practice. Wiley, 2009.
    [41] C. Yang, C. Chen, W. He, R. Cui, and Z. Li. Robot learning system based on adaptive neural control and dynamic movement primitives. IEEE Transactions on Neural Networks and Learning Systems, 30(3):777–787, March 2019.
    [42] D. Xu, D. Zhao, J. Yi, and X. Tan. Trajectory tracking control of omnidirectional wheeled mobile manipulators: Robust neural network-based sliding mode approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(3):788–799, June 2009.
    [43] J. Peng, Z. Yang, and T. Ma. Position/force tracking impedance control for robotic systems with uncertainties based on adaptive jacobian and neural network. Complexity, 2019.
    [44] OptiTrack. Optitrack for robotics. https://optitrack.com/ motion-capture-robotics/.
    [45] Robot Operating System. The application of robot operating system (ros). https://www.ros.org/.

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