簡易檢索 / 詳目顯示

研究生: 楊皓翔
Yang, Hao-Xiang
論文名稱: 使用新型ICP演算法多視角重建實現機器人手臂的噴漆路徑
Implementing Robotic Arm Painting Operations Using Novel ICP Algorithm multi-view Reconstruction
指導教授: 陳美勇
Chen, Mei-Yung
口試委員: 陳美勇
Chen, Mei-Yung
張嘉文
Chang, Chia-Wen
張文哲
Chang, Wen-Jer
口試日期: 2025/01/22
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 47
中文關鍵詞: 六軸機械手臂迭代最近點算法運動路徑三維點雲Open3D
英文關鍵詞: Six-axis robotic arm, Iterative Closest Point (ICP) algorithm, Motion path, 3D point cloud, Open3D
研究方法: 實驗設計法比較研究內容分析法
DOI URL: http://doi.org/10.6345/NTNU202500375
論文種類: 學術論文
相關次數: 點閱:25下載:0
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
  • 這篇論文介紹了一種創新的三維模型噴塗方法,利用 Open3D 多視角重建來優化機械臂輔助的工業噴塗過程。該方法首先通過深度相機從多個角度捕獲詳細的三維點雲,並通過迭代最近點(ICP)算法進行精確對齊和重建。
    這種精細的對齊產生了高度準確的三維模型,能夠引導機械臂進行精確且高效的噴塗應用。此外,我們開發了一種基於生成的點雲模型的往復路徑規劃策略,模擬典型的噴塗運動模式,以提高效率、覆蓋均勻性和應用的一致性。通過實驗評估,我們證明了這種方法顯著提高了噴塗精度,確保均勻的覆蓋和提高過程的可靠性。這使得該方法非常適合於先進的自動噴塗應用,為滿足大規模生產環境中嚴格的工業質量和精度標準提供了一個可靠的解決方案。

    This paper introduces an innovative three-dimensional model painting method that utilizes Open3D multi-view reconstruction to optimize the spray painting process in industrial settings with the assistance of a robotic arm. The method initiates by capturing detailed 3D point clouds through depth cameras from multiple angles, which are then precisely aligned and reconstructed using the Iterative Closest Point (ICP) algorithm. This careful alignment results in highly accurate 3D models that guide the robotic arm for precise and efficient paint application. Additionally, we have developed a back-and-forth path planning strategy based on the generated point cloud model, simulating a typical painting motion pattern that enhances efficiency, coverage uniformity, and application consistency. Through experimental evaluation, we demonstrate that this approach significantly improves painting accuracy, ensuring even coverage and increased reliability across the process. This makes the method ideally suited for advanced automated spray painting applications, offering a robust solution to meet stringent industrial standards for quality and precision in large-scale production environments.

    1 第一章 緒論 1 1.1 前言 1 1.2 文獻回顧 3 1.3 研究動機與目的 5 1.4 論文架構 5 2 第二章 理論基礎 7 2.1 Open 3D 7 2.2 點雲 8 2.3 ICP算法 9 2.4 平均降採樣 14 2.5 平均濾波器 15 3 第三章 系統設計 18 3.1 系統架構 18 3.2 實驗設備 19 3.2.1 Intel RealSense Depth Camera D435i 19 3.2.2 DOBOT NOVA5 六軸機械手臂 19 3.2.3 電腦系統介紹 20 3.3 手眼坐標系關係 20 3.4 新的ICP變體 21 3.5 點雲噴漆路經 25 4 第四章 實驗結果與討論 27 4.1 改良的ICP變體合成 27 4.2 點雲預處理 28 4.3 處理後點雲噴漆路經 30 4.4 與其他研究成果比較 31 5 第五章 結論與未來展望 45 6 參考文獻 46

    [1] Open3D. "Introducing Open3D 0.5.0." *Open3D*, 18 Jan. 2019,https://www.open3d.org/2019/01/18/introducing-open3d-0-5-0/.
    [2] Besl, P.J. and N.D. McKay. Method for registration of 3-D shapes. in Sensor fusion IV: control paradigms and data structures. 2017.. Spie.
    [3] Huei-Yung Lin; Shih-Cheng Liang; Yu-Kai Chen, Robotic Grasping With Multi-View Image Acquisition and Model-Based Pose Estimation, IEEE Robotics and Automation Letters , 2021.
    [4] Xing, S., Jing, F., & Tan, M. (2023). "Reconstruction-Based Hand–Eye Calibration Using Arbitrary Objects," IEEE Transactions on Industrial Informatics, May 2023
    [5] F. Rusinkiewicz and M. Levoy. Efficient variants of the ICP algorithm. In 3-D Digital Imaging and Modeling, 2001
    [6] Park, Q.-Y. Zhou, and V. Koltun, Colored Point Cloud Registration Revisited, ICCV, 2017.
    [7] Y. Guo, M. Bennamoun, F. Sohel, M. Lu, J. Wan, and N. M. Kwok, “A comprehensive performance evaluation of 3D local feature descriptors,” Int. J. Comput. Vis., vol. 116, no. 1, pp. 66–89, Jan. 2016.
    [8] T. Zinsser, J. Schmidt, and H. Niemann, “A refined ICP algorithm for robust 3-D correspondence estimation,” in Proc. Int. Conf. Image Process., vol. 2, Sep. 2003, p. II-695.
    [9] Y. Shiu and S. Ahmad, “Calibration of wrist-mounted robotic sensorsby solving homogeneous transform equations of the form AX=XB,”
    IEEE Trans. Robot. Automat., vol. 5, no. 1, pp. 16–29, Feb. 1989
    [10] K.Daniilidis, “Hand-eye calibration using dual quaternions, ”Int. J. Robot.Res., vol. 18, no. 3, pp. 286–298, 1999
    [11] N. Andreff, R. Horaud, and B. Espiau, “On-line hand-eye calibration,” in Proc. IEEE 2nd Int. Conf. 3-D Digit. Imag. Model., 1999, pp. 430–436.
    [12] K. H. Strobl and G. Hirzinger, “Optimal hand-eye calibration,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2006,
    [13] Z. Zhao, “Hand-eye calibration using convex optimization,” in Proc. IEEE Int. Conf. Robot. Automat., 2011, pp. 2947–2952.
    [14] X. Wang, J. Huang, and H. Song, “Simultaneous robot–world and handeye calibration based on a pair of dual equations,” Measurement, vol. 181,2021
    [15] E. Pedrosa, M. Oliveira, N. Lau, and V. Santos, “A general approachto hand eye calibration through the optimization of atomic transformations,” IEEE Trans. Robot., vol. 37, no. 5, pp. 1619–1633, Oct. 2021
    [16] J. Sock, S. H. Kasaei, L. S. Lopes, and T.-K. Kim, “Multi-view 6D object
    pose estimation and camera motion planning using RGBD images,” inProc. IEEE Int. Conf. Comput. Vis. Workshops (ICCVW), Oct. 2017,
    pp. 2228–2235.
    [17] A. Zeng, S. Song, M. Niessner, M. Fisher, J. Xiao, and T. Funkhouser,“3DMatch: Learning local geometric descriptors from RGB-D recon-structions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),
    Jul. 2017, pp. 199–208.
    [18] K. H. Strobl and G. Hirzinger, “Optimal hand-eye calibration,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2006

    下載圖示
    QR CODE