簡易檢索 / 詳目顯示

回結果列表
研究生： 温梓傑
Wun, Tzu-Chieh
論文名稱： 基於軌跡優化之工業型機械手臂避障研究
Study on Obstacle Avoidance for Industrial Manipulators Based on Trajectory Optimization
指導教授： 鄭銘揚
Cheng, Ming-Yang
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 80
中文關鍵詞： 運動規劃 、軌跡優化 、擬譜法 、避障 、工業型機械手臂
外文關鍵詞： Motion Planning, Trajectory Optimization, Pseudospectral Methods, Obstacle Avoidance, Industrial Manipulator
相關次數： 點閱：77  下載：5 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 隨著智慧工廠的發展趨勢，製造業面臨重大的產業轉型，在智慧製造的領域中，人機協作與擴增實境的結合逐漸受到重視，然而也因此增加操作人員與生產設備發生碰撞等衝突的可能性。有鑑於此，本論文發展一套基於機械手臂關節空間的避障演算法，首先使用擬譜法將軌跡優化問題轉化為非線性規劃問題，接著引入航點限制條件以擴增成多段軌跡優化問題，透過時間—急跳度優化的目標函數，縮短軌跡時長並減少急跳度，抑制機械手臂震顫的現象，接下來分析並建立機械手臂自主避障的限制條件，包含自我防碰撞、避開環境障礙物以及限制機械手臂的工作空間，並將上述避障情況描述為軌跡優化問題中的限制條件。另外為解決非線性求解器陷入局部最佳解的問題，本論文引入了末端效應器姿態限制條件以符合常見的應用情境，並提出「結合RRT-Connect與S-curve之初始軌跡」策略。模擬結果顯示，該策略能在解空間較小的軌跡優化問題中，獲得更佳的軌跡解，而實驗結果亦證實了本論文所提出之軌跡優化避障演算法的有效性、可行性與應用價值。

    With the growing trend of smart factories, the manufacturing industry is facing significant industrial transformation. In the field of smart manufacturing, the combination of human-robot collaboration and augmented reality is gradually gaining attention. However, this also increases the possibility of collisions between human operators and production equipment. Thus, this thesis develops an obstacle avoidance algorithm based on the joint space of industrial manipulators. Firstly, the trajectory optimization problem (TOP) is converted into a nonlinear program using the pseudospectral method. Then, waypoint constraints are introduced to construct a multi-phase TOP. By performing time-jerk optimal trajectory planning, not only can the execution time be shortened, but also the vibration of the manipulators can be reduced. Next, the constraints for the obstacle avoidance are analyzed and established. These constraints include self-collision avoidance, obstacle avoidance, and limitations on the workspace. These constraints of obstacle avoidance are integrated into the TOP. Additionally, end-effector pose constraints are introduced in this thesis to accommodate common application scenarios. Moreover, the “Integrated RRT-Connect and S-curve for Initial Trajectories” strategy is proposed to address the issue of convergence to local optima for the nonlinear solver. Simulation results demonstrate that this strategy achieves better solutions in cases with a smaller feasible solution set. Furthermore, experimental results confirm the effectiveness and feasibility of the proposed obstacle avoidance algorithm based on trajectory optimization.

    中文摘要 I EXTEND ABSTRACT II 誌謝 XVI 目錄 XVIII 表目錄 XXII 圖目錄 XXIII 符號 XXV 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 2 1.3 論文貢獻與架構 4 第二章 機械手臂運動學與動力學 6 2.1 順向運動學模型 7 2.2 逆向運動學模型 10 2.3 動力學模型 16 2.4 本章小結 21 第三章 基於關節空間之軌跡優化 22 3.1 軌跡優化簡介 23 3.1.1 軌跡目標函數 23 3.1.2 軌跡限制條件 23 3.1.2.1 系統動力學限制 23 3.1.2.2 運動力學限制 24 3.1.2.3 路徑限制 24 3.1.2.4 路程狀態與路程輸入限制 24 3.1.2.5 邊界限制 24 3.1.2.6 時間限制 24 3.1.2.7 初始狀態與終點狀態限制 25 3.2 轉化法（Transcription Method） 25 3.2.1 擬譜法 26 3.2.2 拉格朗日重心插值法（Barycentric Lagrange Interpolation） 27 3.2.3 微分矩陣 28 3.2.4 數值積分 31 3.3 基於機械手臂之軌跡優化問題 32 3.3.1 目標函數 33 3.3.2 運動學限制 33 3.3.2.1 關節角度限制 33 3.3.2.2 關節角速度限制 34 3.3.2.3 關節角加度限制 34 3.3.2.4 末端效應器姿態限制 34 3.3.3 邊界限制 34 3.3.4 控制輸入限制 35 3.3.5 系統動力學限制 35 3.3.6 航點限制 35 3.3.6.1 航點位置限制 35 3.3.6.2 航點靜止限制 35 3.3.6.3 連續限制 36 3.3.7 時間限制 36 3.4 本章小結 36 第四章 基於軌跡優化之避障方法 37 4.1 機械手臂之避障限制條件 38 4.1.1 機械手臂自我防碰撞機制 39 4.1.2 環境障礙物防碰撞限制 41 4.1.3 工作空間限制 44 4.2 結合RRT-Connect與S-curve之初始軌跡 45 4.3 軌跡優化避障演算法整體架構 46 4.4 本章小結 48 第五章 模擬與實驗結果分析 49 5.1 實驗設備與場景 50 5.1.1 台達DRV70L六軸工業型機械手臂 50 5.1.1.1 硬體設備 50 5.1.1.2 機械手臂控制器迴路 51 5.1.2 實驗場景與任務介紹 52 5.1.2.1 場景一：M字形避障 53 5.1.2.1.1 環境障礙物參數設定 53 5.1.2.1.2 任務描述與其他參數設定 55 5.1.2.2 場景二：狹窄空間避障 56 5.1.2.2.1 環境障礙物參數設定 56 5.1.2.2.2 任務描述與其他參數設定 57 5.2 模擬介紹 58 5.2.1 模擬環境介紹 58 5.2.2 模擬參數設定 59 5.2.2.1 機械手臂動力學模型參數 59 5.2.2.2 動力學參數之座標轉換 61 5.2.2.3 限制條件參數設定 63 5.3 實驗結果與討論 64 5.3.1 場景一之模擬與實驗結果 64 5.3.1.1 末端效應器姿態限制之有效性驗證 68 5.3.2 場景二之模擬與實驗結果 69 5.3.2.1 比較初始軌跡對於非線性求解器之影響 73 5.4 本章小結 74 第六章 結論與建議 75 6.1 結論 75 6.2 未來展望與建議 76 參考文獻 77

    [1]P. K. R. Maddikunta, Q. V. Pham, P. B, N. Deepa, K. Dev, T. R. Gadekallu, R. Ruby, and M. Liyanage, “Industry 5.0: A Survey on Enabling Technologies and Potential Applications,” Journal of Industrial Information Integration, vol. 26, Mar. 2022.
    [2]Ryan Tsai (2023，4月26日)，成功轉型智慧工廠　未來可能成製造業「必考題」，EE Times Taiwan。檢索日期2023年6月28日，取自https://www.eettaiwan.com/20230426nt31-successful-transformation-of-smart-factory/.
    [3]2022/2023 產業技術白皮書，經濟部技術處，2022。
    [4]施鑫澤、林裕洋(2022，6月2日)，台達 5G 智慧工廠啟用週年，CIO Taiwan。檢索日期2023年6月28日，取自https://www.cio.com.tw/delta-5g-smart-factory-enablement-anniversary/.
    [5]UR, “How Augmented Reality and Cobots Drive the Next Wave of Automation,” 2022. [Online]. Available: https://www.universal-robots.com/blog/how-augmented-reality-and-cobots-drive-the-next-wave-of-automation/.
    [6]O. Khatib, “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” in Proceedings of 1985 IEEE International Conference on Robotics and Automation, St. Louis , USA, 1985, pp. 500-505.
    [7]黃品瑄，應用 S-curve 與人工勢場法於搭載機械手臂自走車系統之避障研究，碩士論文，國立成功大學，電機工程學系，2022。
    [8]S. M. LaValle, “Rapidly-exploring Random Trees: A New Tool for Path Planning,” Department of Computer Science, Iowa State University, Tech. Rep., 1998.
    [9]J. J. Kuffner and S. M. LaValle, “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” in Proceedings of 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), San Francisco, USA, 2000, pp. 995-1001.
    [10]S. Karaman and E. Frazzoli, “Sampling-based Algorithms for Optimal Motion Planning,” The International Journal of Robotics Research, vol. 30, no. 7, pp. 846-894, Jun. 2011.
    [11]李權哲，協作型機械臂動態障礙物即時閃避之全方位避碰路徑規劃，碩士論文，國立陽明交通大學，電控工程研究所，2022。
    [12]B. Sangiovanni, A. Rendiniello, G. P. Incremona, A. Ferrara, and M. Piastra, “Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators,” in Proceedings of 2018 European Control Conference (ECC), Limassol, Cyprus, 2018, pp. 2063-2068.
    [13]T. Zhang, K. Zhang, J. Lin, W. Y. G. Louie, and H. Huang, “Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments,” IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 65-72, Jan. 2022.
    [14]M. Rubagotti, B. Sangiovanni, A. Nurbayeva, G. P. Incremona, A. Ferrara, and A. Shintemirov, “Shared Control of Robot Manipulators With Obstacle Avoidance: A Deep Reinforcement Learning Approach,” IEEE Control Systems Magazine, vol. 43, no. 1, pp. 44-63, Feb. 2023.
    [15]N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, “CHOMP: Gradient Optimization Techniques for Efficient Motion Planning,” in Proceedings of 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, pp. 489-494.
    [16]M. Kalakrishnan, S. Chitta, E. Theodorou, P. Pastor, and S. Schaal, “STOMP: Stochastic Trajectory Optimization for Motion Planning,” in Proceedings of 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 2011, pp. 4569-4574.
    [17]J. Schulman, J. Ho, A. X. Lee, I. Awwal, H. Bradlow, and P. Abbeel, “Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization,” in Proceedings of Robotics: Science and Systems, Berlin, Germany, 2013, pp. 1-10.
    [18]C. Wang, J. Bingham, and M. Tomizuka, “Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization,” in Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 8113-8120.
    [19]S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends® in Machine Learning, vol. 3, no. 1, pp. 1-122, Jan. 2011.
    [20]Y. Zhao, H. C. Lin, and M. Tomizuka, “Efficient Trajectory Optimization for Robot Motion Planning,” in Proceedings of 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, 2018, pp. 260-265.
    [21]Y. Wen and P. Pagilla, “Path-Constrained and Collision-Free Optimal Trajectory Planning for Robot Manipulators,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 763-774, Apr. 2023.
    [22]M. A. Patterson, and A. V. Rao, “GPOPS-II: A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using hp-Adaptive Gaussian Quadrature Collocation Methods and Sparse Nonlinear Programming,” ACM Transactions on Mathematical Software, vol. 41, no. 1, pp. 1-37, Oct. 2014.
    [23]J. J. Craig, Introduction to Robotics: Mechanics and Control. Hoboken, NJ, USA: Pearson Prentice Hall, 2009.
    [24]楊士賢，基於電腦視覺與危險能量場之工業型機械手臂避障研究，碩士論文，國立成功大學，電機工程學系，2020。
    [25]吳如峰，工業用六軸機械手臂之基於影像視覺伺服架構研究，碩士論文，國立成功大學，電機工程學系，2015。
    [26]R. Featherstone, “Forward Dynamics — Propagation Methods,” in Rigid Body Dynamics Algorithms, Boston, MA, USA: Springer US, 2008, pp. 119-139.
    [27]M. Kelly, “An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation,” Society for Industrial and Applied Mathematics Review, vol. 59, no. 4, pp. 849-904, Jan. 2017.
    [28]B. Donald, P. Xavier, J. Canny, and J. Reif, “Kinodynamic Motion Planning,” Journal of the ACM, vol. 40, no. 5, pp. 1048-1066, Nov. 1993.
    [29]S. M. LaValle, Planning Algorithms. New York, NY, USA: Cambridge University Press, 2006.
    [30]A. Rao, “A Survey of Numerical Methods for Optimal Control,” Advances in the Astronautical Sciences, vol. 135, no. 1, Jan. 2010.
    [31]J. P. Berrut and L. N. Trefethen, “Barycentric Lagrange Interpolation,” Society for Industrial and Applied Mathematics Review, vol. 46, no. 3, pp. 501-517, Sep. 2004.
    [32]R. Baltensperger and M. R. Trummer, “Spectral Differencing with a Twist,” Society for Industrial and Applied Mathematics Journal on Scientific Computing, vol. 24, no. 5, pp. 1465-1487, Jan. 2003.
    [33]J. Waldvogel, “Fast Construction of the Fejér and Clenshaw–Curtis Quadrature Rules,” BIT Numerical Mathematics, vol. 46, no. 1, pp. 195-202, Mar. 2006.
    [34]I. A. Șucan and S. Chitta, “MoveIt,” 2023. [Online]. Available: https://moveit.ros.org/.
    [35]J. A. E. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi: A Software Framework for Nonlinear Optimization and Optimal Control,” Mathematical Programming Computation, vol. 11, no. 1, pp. 1-36, Jul. 2018.
    [36]A. Wächter and L. T. Biegler, “On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming,” Mathematical Programming, vol. 106, no. 1, pp. 25-57, Mar. 2006.
    [37]T. A. Driscoll, N. Hale, and L. N. Trefethen, Eds, Chebfun Guide. Oxford, U.K.: Pafnuty Publications, 2014.
    [38]The MathWorks, Robotics System Toolbox for Use with MATLAB, The MathWorks, 2022.
    [39]K. Tracy, T. A. Howell, and Z. Manchester, “Differentiable Collision Detection for A Set of Convex Primitives,” arXiv preprint arXiv:2207.00669, 2023.
    [40]S. Lloyd, R. A. Irani, and M. Ahmadi, “Fast and Robust Inverse Kinematics of Serial Robots Using Halley’s Method,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2768-2780, Oct. 2022.

    下載圖示 校內：2024-08-31公開
    校外：2024-08-31公開
    QR CODE