Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking

被引:21
作者
Hong, Bo [1 ]
Jia, Aiting [1 ]
Hong, Yuxiang [2 ]
Li, Xiangwen [1 ]
Gao, Jiapeng [1 ]
Qu, Yuanyuan [1 ]
机构
[1] Xiangtan Univ, Coll Mech Engn, Xiangtan 411105, Peoples R China
[2] China Jiliang Univ, Coll Mech & Elect Engn, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金;
关键词
3D zigzag-line welding seams; welding seam tracking; extraction of pose information of welding seams; laser displacement sensor; point cloud segmentation;
D O I
10.3390/s21020375
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using the rho-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.
引用
收藏
页码:1 / 19
页数:19
相关论文
共 26 条
[1]   IRoSim: Industrial Robotics Simulation Design Planning and Optimization platform based on CAD and knowledgeware technologies [J].
Baizid, Khelifa ;
Cukovic, Sasa ;
Iqbal, Jamshed ;
Yousnadj, Ali ;
Chellali, Ryad ;
Meddahi, Amal ;
Devedzic, Goran ;
Ghionea, Ionut .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2016, 42 :121-134
[2]   Model analysis and experimental technique on computing accuracy of seam spatial position information based on stereo vision for welding robot [J].
Chen, Xi-Zhang ;
Huang, Yu-Ming ;
Chen, Shan-ben .
INDUSTRIAL ROBOT-AN INTERNATIONAL JOURNAL, 2012, 39 (04) :349-356
[3]   Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding [J].
Dinham, Mitchell ;
Fang, Gu .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2013, 29 (05) :288-301
[4]   Study on Stereo Vision for 3D Reconstruction of Welding Seam [J].
Du, Jun ;
Yong, Lingyu ;
Sun, Mei ;
Ge, Jiasheng .
ADVANCES IN APPLIED SCIENCES AND MANUFACTURING, PTS 1 AND 2, 2014, 850-851 :212-+
[5]  
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
[6]   RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY [J].
FISCHLER, MA ;
BOLLES, RC .
COMMUNICATIONS OF THE ACM, 1981, 24 (06) :381-395
[7]   DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation [J].
Gan, Junhao ;
Tao, Yufei .
SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2015, :519-530
[8]  
Gunawan A., 2013, A Faster Algorithm for DBSCAN
[9]  
Guodong Peng, 2018, Journal of Physics: Conference Series, V1074, DOI 10.1088/1742-6596/1074/1/012001
[10]  
Jia T, 2017, IEEE ANN INT CONF CY, P179, DOI 10.1109/CYBER.2017.8446515