基于YOLO模型的柔索并联机器人移动构件快速定位方法

doi:10.3901/JME.2019.03.064

机械工程学报 ›› 2019, Vol. 55 ›› Issue (3): 64-72.doi: 10.3901/JME.2019.03.064

基于YOLO模型的柔索并联机器人移动构件快速定位方法

訾斌, 尹泽强, 李永昌, 赵涛

合肥工业大学机械工程学院合肥 230009

收稿日期:2018-06-27 修回日期:2018-10-31 出版日期:2019-02-05 发布日期:2019-02-05
作者简介:訾斌,男,1975年出生,博士,教授。主要研究方向为机电控制及自动化。E-mail:binzi.cumt@163.com;尹泽强,男,1993年出生,男,硕士研究生。主要研究方向为柔索并联机器人动力学与控制。E-mail:xiaoqiang22ll@sina.com
基金资助:
国家重点研发计划（2017YFB1303900）和国家自然科学基金（51575150，91748109）资助项目。

Fast Mobile Component Location Method for Cable-driven Parallel Robots Based on YOLO Model

ZI Bin, YIN Zeqiang, LI Yongchang, ZHAO Tao

School of Mechanical Engineering, Hefei University of Technology, Hefei 230009

Received:2018-06-27 Revised:2018-10-31 Online:2019-02-05 Published:2019-02-05

摘要/Abstract

摘要： 针对柔索并联机器人移动构件实时定位问题，提出一种基于YOLO目标检测模型的柔索并联机器人移动构件快速定位方法。首先根据YOLO目标检测模型设计深度卷积神经网络结构，根据PASCAL VOC数据格式构建自己的数据集，并在该数据集上训练及测试模型，然后将工业摄像机采集得到的图像数据输入模型中进行标靶检测，记录标靶的类别和位置。分析标靶的颜色特征，并将标靶图像进行二值化，进一步计算出柔索并联机器人的精确位置。试验表明该方法能对图像中的目标进行准确分类和定位，定位误差在1°以内，图片处理帧率可达33帧，满足实时性要求，同时算法具有良好的准确性和有效性。

关键词: 快速定位方法, 目标检测, 柔索并联机器人, 深度学习

Abstract: Aiming at the problem of real-time mobile component localization of cable-driven parallel robot, a fast mobile component location method based on YOLO object detection model is proposed. Firstly, the deep convolution neural network structure is designed, a data set is constructed in PASCAL VOC format, and the model is trained and tested on that data set. Then the image data collected by the industrial camera are inputted into the model to detect the target, record the category and position of the target, analyze the color characteristics of the target and binarization. The precise position of the mobile component of the cable-driven parallel robot is further calculated. The experimental results show that the method can classify and locate the target accurately. The positioning error is less than 1°, and the frame rate of image processing can reach 33 frames, which can meet the real-time requirements. At the same time, the algorithm has good accuracy and effectiveness.

Key words: cable-driven parallel robot, deep learning, fast location method, object detection

中图分类号:

TP242

訾斌, 尹泽强, 李永昌, 赵涛. 基于YOLO模型的柔索并联机器人移动构件快速定位方法[J]. 机械工程学报, 2019, 55(3): 64-72.

ZI Bin, YIN Zeqiang, LI Yongchang, ZHAO Tao. Fast Mobile Component Location Method for Cable-driven Parallel Robots Based on YOLO Model[J]. Journal of Mechanical Engineering, 2019, 55(3): 64-72.

参考文献

[1] JAMSHIDIFAR H,KHAJEPOUR A,FIDAN B,et al. Kinematically constrained redundant cable-driven parallel robots:Modeling,redundancy analysis,and stiffness optimization[J]. IEEE/ASME Transactions on Mechatronics,2017,22(2):921-930.
[2] QIAN S,ZI B,SHANG W W,et al. A review on cable-driven parallel robots[J]. Chinese Journal of Mechanical Engineering,2018,31(1):66.
[3] 孔令富,王月明,赵立强. 并联机器人双目主动视觉目标定位的研究[J]. 计算机集成制造系统,2007,13(11):2285-2288. KONG Lingfu,WANG Yueming,ZHAO Liqiang. Space target positioning based on binocular active vision system of parallel robot[J]. Computer Integrated Manufacturing Systems,2007,13(11):2285-2288.
[4] GAGLIARDINI L,CARO S,GOUTTEFARDE M,et al. Discrete reconfiguration planning for cable-driven parallel robots[J]. Mechanism and Machine Theory,2016,100:313-337.
[5] 訾斌,王炳尧,刘浩,等. 可重构柔索并联机器人协同避障方法研究[J]. 仪器仪表学报,2017,38(3):593-601. ZI Bin,WANG Bingyao,LIU Hao,et al. Study on the collaborative obstacle avoidance method for reconfigurable cable driven parallel robot[J]. Chinese Journal of Scientific Instrument,2017,38(3):593-601.
[6] AFLAKIAN A,SAFARYAZDI A,MASOULEH M T,et al. Experimental study on the kinematic control of a cable suspended parallel robot for object tracking purpose[J]. Mechatronics,2018,50:160-176.
[7] MERLET J P. Simulation of discrete-time controlled cable-driven parallel robots on a trajectory[J]. IEEE Transactions on Robotics,2017,33(3):675-688.
[8] 訾斌,曹建斌,钱森. 混合驱动柔索并联机器人系统集成设计[J]. 机械工程学报,2012,48(21):28-35. ZI Bin,CAO Jianbin,QIAN Sen. Integrated design of hybrid-driven-based cable parallel manipulator system[J]. Journal of Mechanical Engineering,2012,48(21):28-35.
[9] 付生鹏,赵吉宾,夏仁波,等. 基于成像模拟法的镜面体表面三维测量[J]. 机械工程学报,2015,51(10):17-24. FU Shengpeng,ZHAO Jibin,XIA Renbo,et al. Imaging simulation method for specular surface measurement[J]. Journal of Mechanical Engineering,2015,51(10):17-24.
[10] 黄丹平,廖世鹏,于少东,等. 基于机器视觉瓦楞纸板自动计数系统研究[J]. 机械工程学报,2017,53(10):79-86. HUANG Danping,LIAO Shipeng,YU Shaodong,et al. Research on automatic counting system corrugated paper board based on machine vision[J]. Journal of Mechanical Engineering,2017,53(10):79-86.
[11] 尹宏鹏,陈波,柴毅,等. 基于视觉的目标检测与跟踪综述[J]. 自动化学报,2016,42(10):1466-1489. YIN Hongpeng,CHEN Bo,CHAI Yi,et al.Vision-based object detection and tracking:A review[J]. ACTA AUTOMATICA SINICA:2016,42(10):1466-1489.
[12] HINTON G E,SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science,2006,313(5786):504-507.
[13] KRIZHEVSKY A,SUTSKEVER I,HINTON G E. Advances in neural information processing systems[M]. Cambridge:MIT Press,2012.
[14] REN S,HE K,GIRSHICK R,et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[15] REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:Unified,real-time object detection[C]//Computer Vision and Pattern Recognition (CVPR),June 27-30,2016,Las Vegas. Washington:IEEE Computer Society,2016,779-788.
[16] REDMON J,FARHADI A. YOLO9000:Better,faster,stronger[C]//Computer Vision and Pattern Recognition (CVPR),July 22-25,Hawaii Convention Center,Honolulu. Washington:IEEE Computer Society,2017:6517-6525.
[17] LIU W,ANGUELOV D,ERHAN D,et al. SSD:Single shot multibox detector[C]//European conference on computer vision,October 8-16,2016,Amsterdam. Cham:Springer,2016:21-37.
[18] 伍锡如,黄国明,孙立宁. 基于深度学习的工业分拣机器人快速视觉识别与定位算法[J]. 机器人,2016,38(6):711-719. WU Xiru,HUANG Guoming,SUN Lining. Fast visual identification and location algorithm for industrial sorting robots based on deep learning[J]. Robot:2016,38(6):711-719.
[19] 杜学丹,蔡莹皓,鲁涛,等. 一种基于深度学习的机械臂抓取方法[J]. 机器人,2017,39(6):820-828,837. DU Xuedan,CAI Yinghao,LU Tao,et al. A robotic grasping method based on deep learning[J]. Robot:2017,39(6):820-828,837.
[20] GOODFELLOW I,BENGIO Y,COURVILLE A,et al. Deep learning[M]. Cambridge:MIT press,2016.

基于YOLO模型的柔索并联机器人移动构件快速定位方法

Fast Mobile Component Location Method for Cable-driven Parallel Robots Based on YOLO Model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

[1]	裴洪, 胡昌华, 司小胜, 张建勋, 庞哲楠, 张鹏. 基于机器学习的设备剩余寿命预测方法综述[J]. 机械工程学报, 2019, 55(8): 1-13.
[2]	雷亚国, 杨彬, 杜兆钧, 吕娜. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(7): 1-8.
[3]	王奉涛, 刘晓飞, 敦泊森, 邓刚, 韩清凯, 李宏坤. 基于萤火虫优化的核自动编码器在中介轴承故障诊断中的应用[J]. 机械工程学报, 2019, 55(7): 58-64.
[4]	李川, 张绍辉, José Valente de Oliveira. 基于次优网络深度学习的3D打印机故障诊断[J]. 机械工程学报, 2019, 55(7): 73-80.
[5]	姜洪开, 邵海东, 李兴球. 基于深度学习的飞行器智能故障诊断方法[J]. 机械工程学报, 2019, 55(7): 27-34.
[6]	汪久根, 柯梁亮. 基于残差网络的RV减速器故障诊断[J]. 机械工程学报, 2019, 55(3): 73-80.
[7]	吕杰, 罗芳颖, 袁泽剑. 目标搜索与识别的视觉注意网络与学习方法[J]. 机械工程学报, 2019, 55(11): 123-130.
[8]	李鹏越, 田建东, 王国霖, 李小毛, 唐延东, 吴成东. 面向机器人环境共融的图像去雪算法[J]. 机械工程学报, 2019, 55(11): 98-104.
[9]	雷亚国, 贾峰, 周昕, 林京. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21): 49-56.
[10]	訾斌;曹建斌;钱森. 混合驱动柔索并联机器人系统集成设计[J]. , 2012, 48(21): 28-35.
[11]	訾斌;朱真才;曹建斌. 混合驱动柔索并联机器人的设计与分析[J]. , 2011, 47(17): 1-8.