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基于深度分层特征的激光视觉焊缝检测与跟踪系统研究

Research of Laser Vision Seam Detection and Tracking System Based on Depth Hierarchical Feature

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摘要

针对自适应性低的焊缝跟踪系统在实际焊接环境中易受噪声干扰的问题, 结合深度卷积神经网络强大的特征表达能力和自学习功能, 研究了基于深度分层特征的焊缝检测和跟踪系统, 该系统可精确地从噪声污染的时序图像中确定焊缝位置。为彻底解决焊枪依循计算轨迹运动所出现的抖振问题, 设计了模糊免疫自适应的智能跟踪控制算法。实验结果显示, 在强烈弧光和飞溅的干扰下, 传感器测量频率达20 Hz, 焊缝跟踪精度约为0.2060 mm,且焊接过程中焊枪末端运行平稳。该系统能实现焊缝平滑的实时跟踪, 抗干扰能力强, 焊缝轨迹跟踪准确, 能满足焊接应用要求。

Abstract

Aimed at the problem that the seam tracking system with low adaptability is sensitive to noise in the actual welding environment, and combined with the strong feature expression ability and self-learning function of the depth convolution neural network, a welding seam detection and tracking system based on depth hierarchical feature is studied. The location of seam from noise-contaminated serial images is accurately determined by this system. A fuzzy immune self-adaptive intelligent tracking control algorithm is designed to completely solve the chattering problem of welding torch following the calculated trajectory. The experimental results show that, under the interference of strong arc and splash, the metrical frequency of sensor can be up to 20 Hz, the tracking accuracy of the welding seam is about 0.2060 mm, and the end of the welding torch runs smoothly during the process of welding. The system can realize real-time tracking of the welding seam, has strong anti-interference ability, and can accurately track the trajectory of the welding seam, which can meet the requirements of welding application.

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中图分类号:TP242.2

DOI:10.3788/cjl201744.0402009

所属栏目:激光制造

基金项目:国家科技重大专项(2015ZX04005006-03)、广东省科技重大专项(2014B090921004)、广东省战略性新兴产业核心技术攻关项目(2011A091101001)、广州市科技重大项目(2014Y2-00014)

收稿日期:2016-11-09

修改稿日期:2016-12-17

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作者单位    点击查看

邹焱飚:华南理工大学机械与汽车工程学院, 广东 广州 510640
周卫林:华南理工大学机械与汽车工程学院, 广东 广州 510640
陈向志:华南理工大学机械与汽车工程学院, 广东 广州 510640

联系人作者:邹焱飚(ybzou@scut.edu.cn)

备注:邹焱飚(1971-), 男, 博士, 副教授, 主要从事机器人理论及工程应用方面的研究。

【1】Li Lin, Lin Bingqiang, Zou Yanbiao. Study on seam tracking system based on stripe type laser sensor and welding robot[J]. Chinese J Lasers, 2015, 42(5): 0502005.
李 琳, 林炳强, 邹焱飚. 基于条纹式激光传感器的机器人焊缝跟踪系统研究[J]. 中国激光, 2015, 42(5): 0502005.

【2】Yang Guowei, Sun Changku, Wang Peng. Real-time stroboscopic laser fringe-pattern projection system[J]. Acta Optica Sinica, 2014, 34(11): 1112002.
杨国威, 孙长库, 王 鹏. 频闪激光光栅条纹实时投射系统[J]. 光学学报, 2014, 34(11): 1112002.

【3】Zhang Jie. Research on seam tracking system of special welding machine based on structured-light vision sensor[D]. Nanjing: Southeast University, 2011.
张 捷. 基于结构光视觉的焊接专机焊缝跟踪系统的研究[D]. 南京: 东南大学, 2011.

【4】Huang Shisheng, Qian Yingxue. Welding seam detecting algorithm based on the ART artificial neural network[J]. Chinese Journal of Mechanical Engineering, 1994, 30(2): 93-98.
黄石生, 钱迎雪. 基于ART人工神经网络的焊缝跟踪检测算法[J]. 机械工程学报, 1994, 30(2): 93-98.

【5】Gong Yefei, Dai Xianzhong, Li Xinde, et al. Robust joint tracking with structured-light vision sensing[J]. Transactions of the China Welding Institution, 2010, 31(12): 61-64.
龚烨飞, 戴先中, 李新德, 等. 结构光视觉稳健焊接接头跟踪[J]. 焊接学报, 2010, 31(12): 61-64.

【6】Chen Haiyong, Sun Hexu, Xu De. An image feature extraction method for a certain of narrowgap weld seam[J]. Transactions of the China Welding Institution, 2012, 33(1): 61-64.
陈海永, 孙鹤旭, 徐 德. 一类窄焊缝的结构光图像特征提取方法[J]. 焊接学报, 2012, 33(1): 61-64.

【7】Lee J P, Wu Q Q, Park M H, et al. A study on modified Hough algorithm for image processing in weld seam tracking system[J]. Advanced Materials Research, 2015, 1088(11): 824-828.

【8】Ding Y, Huang W, Kovacevic R. An on-line shape-matching weld seam tracking system[J]. Robotics and Computer-Integrated Manufacturing, 2016, 42: 103-112.

【9】He Y, Chen Y, Xu Y, et al. Autonomous detection of weld seam profiles via a model of saliency-based visual attention for robotic arc welding[J]. Journal of Intelligent & Robotic Systems, 2016, 81(3-4): 395-406.

【10】Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image databas[C]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248-255.

【11】Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014.

【12】Shen R, Gou G, Cheng I, et al. Active calibration[M]. New York: Springer, 2014.

【13】Xie Zexiao, Chen Wenzhu, Chi Shukai, et al. Industrial robot positioning system based on the guidance of the structured-light vision[J]. Acta Optica Sinica, 2016, 36(10): 1015001.
解则晓, 陈文柱, 迟书凯, 等. 基于结构光视觉引导的工业机器人定位系统[J]. 光学学报, 2016, 36(10): 1015001.

【14】Wu C, Fan W, He Y, et al. Handwritten character recognition by alternately trained relaxation convolutional neural network[C]. 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014: 291-296.

【15】Schlosser J, Chow C K, Kira Z. Fusing LIDAR and images for pedestrian detection using convolutional neural networks[C]. 2016 IEEE International Conference on Robotics and Automation, 2016: 2198-2205.

【16】Sun Rui, Zhang Guanghai, Gao Jun. Pedestrian recognition method based on depth hierarchical feature representation[J]. Journal of Electronics and Information Technology, 2016, 38(6): 1528-1535.
孙 锐, 张广海, 高 隽. 基于深度分层特征表示的行人识别方法[J]. 电子与信息学报, 2016, 38(6): 1528-1535.

【17】Hariharan B, Arbelaez P, Girshick R, et al. Hypercolumns for object segmentation and fine-grained localization[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 447-456.

【18】Movshovitz-Attias Y, Boddeti V N, Wei Z, et al. 3d poseby-detection of vehicles via discriminatively reduced ensembles of correlation filters[C]. British Machine Vision Conference, 2014.

【19】He Yujie, Li Min, Zhang Jinli, et al. Infrared small target detection method based on correlation filter[J]. Acta Optica Sinica, 2016, 36(5): 0512001.
何玉杰, 李 敏, 张金利, 等. 基于相关滤波器的红外弱小目标检测算法[J]. 光学学报, 2016, 36(5): 0512001.

【20】Gao Fengxin. Vision-based curve tracking system using fuzzy controller[J]. Control Engineering of China, 2016, 23(1): 149-152.
高风昕. 基于模糊控制器的曲线焊缝视觉跟踪系统[J]. 控制工程, 2016, 23(1): 149-152.

【21】Xiong Zhonggang, Ye Zhenhuan, He Juan, et al. Small agricultural machinery path intelligent tracking control based on fuzzy immune PID[J]. Robot, 2015, 37(2): 212-223.
熊中刚, 叶振环, 贺 娟, 等. 基于免疫模糊PID的小型农业机械路径智能跟踪控制[J]. 机器人, 2015, 37(2): 212-223.

【22】Zhang H, Hu J, Bu W. Research on fuzzy immune self-adaptive PID algorithm based on new Smith predictor for networked control system[J]. Mathematical Problems in Engineering, 2015, 2015: 1-6.

【23】Zhang Weiwei, Wang Jing, Wang Hui, et al. Research on the variable universe fuzzy algorithm of chaotic systems[J]. Acta Physica Sinica, 2011, 60(1): 111-119.
张巍巍, 王 京, 王 慧, 等. 混沌系统的变论域模糊控制算法研究[J]. 物理学报, 2011, 60(1): 111-119.

引用该论文

Zou Yanbiao,Zhou Weilin,Chen Xiangzhi. Research of Laser Vision Seam Detection and Tracking System Based on Depth Hierarchical Feature[J]. Chinese Journal of Lasers, 2017, 44(4): 0402009

邹焱飚,周卫林,陈向志. 基于深度分层特征的激光视觉焊缝检测与跟踪系统研究[J]. 中国激光, 2017, 44(4): 0402009

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