[1]王西志,管声启,张理博,等.基于视觉引导的工业棒材上料系统研究[J].机械与电子,2023,41(05):19-23.
 WANG Xizhi,GUAN Shengqi,ZHANG Libo,et al.Research on Industrial Bar Feeding System Based on Visual Guidance[J].Machinery & Electronics,2023,41(05):19-23.
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基于视觉引导的工业棒材上料系统研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年05期
页码:
19-23
栏目:
设计与研究
出版日期:
2023-05-25

文章信息/Info

Title:
Research on Industrial Bar Feeding System Based on Visual Guidance
文章编号:
1001-2257 ( 2023 ) 05-0019-05
作者:
王西志管声启张理博刘 通郝振虎
西安工程大学机电工程学院,陕西 西安 710048
Author(s):
WANG Xizhi GUAN Shengqi ZHANG Libo LIU Tong HAO Zhenhu
( School of Mechanical and Electronic Engineering , Xi ’an Polytechnic University , Xi ’an 710048 , China )
关键词:
上料系统深度学习位姿检测两指机械手
Keywords:
feeding system deep learning pose detection two finger manipulat
分类号:
TP391.4 ; TP241
文献标志码:
A
摘要:
为了提高生产效率,设计一种基于视觉引导的工业棒材上料系统。首先,为了实现视觉引导进行工业棒材上料,设计了工业棒材上料总体方案,并对上料机械结构模型进行选型设计。然后,为了实现棒材的自动 识别和位姿 检测,提出 了一种基于改进 YOLOv5 的旋转目标 识别与定位 算法。该方法在 YOLOv5 主干特征网络上,添加高效 ECA 通道注意力机制模块,利用其避免降维,并通过适当跨通道交互策略提高特征提取能力;为了增强不同尺度的特征信息融合,将原特征增强网络替换成 BiFPN 加权双向特征金字塔网络,进行自上而下和自下而上的多尺度特征融合,提高棒材识别准确率并获取平面位置信息;在此基础上,采用双目视觉进行立体匹配获取棒材的深度位置信息,最终实现棒材立体位姿检测。对所提上料系统进行实验验证,棒材识别的平均精度为 99.4% ,抓取棒材成功率达到 90% 及以上。
Abstract:
In order to improve the production efficiency , this paper designs an industrial bar feeding system based on visual guidance.First of all , in order to realize the visual guidance of industrial bar feeding , the overall scheme of industrial bar feeding is designed , and the selection and design of the feeding mechanical structure model are designed.Then , in order to realize automatic bar recognition and pose detection , a rotating target recognition and location algorithm based on improved YOLOv5 is proposed.In this method , an efficient ECA channel attention mechanism module is added to the YOLOv5 backbone feature network , and the feature extraction ability is improved by using its avoidance reduction and appropriate cross-channel interaction strategy.In order to enhance the feature information fusion of different scales , the original feature enhancement network was replaced with BiFPN weighted bidirectional feature pyramid network , and the top-down and bottom-up multi-scale feature fusion was carried out to improve the bar recognition accuracy and obtain the plane position information.On this basis , the depth and position information of the bar is obtained by stereo matching with binocular vision , and finally the stereo pose detection of the bar is realized.Through the experimental verification of the feeding system in this paper , the average accuracy of bar recognition is 99.4% , and the success rate of grasping bar is 90% or above.

参考文献/References:

[ 1 ] 蒲娟,陈勇 . 棒料切割机自动上料机构设计与实施[ J ] .机械工程师,2019 ( 9 ): 126-127 , 130.

[ 2 ] 刘亚欣,王斯瑶,姚玉峰,等 . 机器人抓取检测技术的研究现状[ J ] . 控制与决策, 2020 , 35 ( 12 ): 2817-2828.
[ 3 ] 林义忠,陈旭 . 基于机器视觉的机器人定位抓取的研究进展[ J ] . 自动化与仪器仪表, 2021 ( 3 ): 9-12.
[ 4 ] 翟敬梅,董鹏飞,张铁 . 基于视觉引导的工业机器人定位抓取系统设计[ J ] . 机械设计与研究, 2014 , 30 ( 5 ): 45-49.
[ 5 ] 单慧勇,张程皓,李晨阳,等 . 鱼加工生产线头尾定向调理上料系统设计与试验[ J ] . 中国农机化学报, 2021 , 42( 3 ): 91-97.
[ 6 ] 张涛,吕庆海 . 车轮生产线中基于视觉引导机械手定位抓取系统设计[ J ] . 电子测量技术, 2019 , 42 ( 16 ): 56-61.
[ 7 ] 曾劲松,薛文凯,徐博凡 . 双目视觉引导机器人定位抓取技术的研究[ J ] . 组合机床与自动化加工技术, 2019( 1 ): 131-133 , 137.
[ 8 ] 彭宇升,孙勇,徐超,等 . 航空锻造柔性自动上料机视觉定位系统[ J ] . 锻压技术, 2021 , 46 ( 12 ): 174-182.
[ 9 ] 孙江宏,何宇凡,高锋,等 . 灵巧式机械手研究现状综述[ J ] . 科学技术与工程, 2021 , 21 ( 26 ): 11005-11014.
[ 10 ] 王杰,管声启,曹帅 . 基于 MATLAB 的主操作手运动学分析及仿真[ J ] . 西安工程大学学报, 2018 , 32 ( 2 ):222-229.
[ 11 ] 温永璐,刘天湖,李桂棋,等 . 智能剖竹机上料机械手的设计优化[ J ] . 机械设计, 2021 , 38 ( 2 ): 54-59.
[ 12 ] 李健洪,马平,刘杰,等 . 上料桁架机械手刚柔耦合运动特性研究[ J ] . 机床与液压, 2019 , 47 ( 23 ): 11-16.
[ 13 ] 熊晓松,段伟,周丽红 . 柔性生产线上双目视觉定位技术[ J ] . 工具技术, 2021 , 55 ( 11 ): 108-111.
[ 14 ] 周旗开,张伟,李东锦,等 . 基于改进 YOLOv5s 的光学遥感图像舰船分类检测方法[ J ] . 激光与光电子学进展,2022 , 59 ( 16 ): 476-483.
[ 15 ] 安胜彪,娄慧儒,陈书旺,等 . 基于深度学习的旋转目标检测方法研究进展[ J ] . 电子测量技术,2021 , 44( 21 ): 168-178.
[ 16 ] 唐建宇,唐春晖 . 基于旋转框和注意力机制的遥感图像目标检测算法[ J ] . 电子测量技术, 2021 , 44 ( 13 ): 114-120.
[ 17 ] SHI P F , ZHAO Z X , FAN X N , et al.Remote sensing image object detection based on angle classification [ J ] .IEEE Access , 2021 , 9 : 118696-118707.
[ 18 ] 尹晨阳,职恒辉,李慧斌 . 基于深度学习的双目立体匹配方法综述[ J ] . 计算机工程, 2022 , 48 ( 10 ): 1-12.
[ 19 ] ZHANG B , ZHU D L.Local stereo matching : an adaptive weighted guided image filtering-based approach [ J ] .International journal of pattern recognition and artificial intelligence , 2021 , 35 ( 3 ): 2154010.
[ 20 ] 苗猛 . 双目视觉系统在盘类零件上料机器人中的应用研究[ D ] . 重庆:重庆大学,2018.
[ 21 ] DU Y C , TARYUDI T , TSAI C T , et al.Eye-to-hand robotic tracking and grabbing based on binocular vision[ J ] .Microsystem technologies , 2021 , 27 ( 4 ): 1699-1710.
[ 22 ] 管声启,于资江,王盈余,等 . 航空插座装配系统的末端执行器结构设计[ J ] . 西安工程大学学报, 2022 , 36( 6 ): 93-99.

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备注/Memo

备注/Memo:
收稿日期: 2022-10-11
基金项目:西安市创新能力强基计划 人工智能技术攻关项目( 21RGZN0021 )
作者简介:王西志 ( 1998- ),男,河北邢台人,硕士研究生,研究方向为机器人视觉;管声启 ( 1971- ),男,安徽安庆人,博士,教授,研究方向为机械零件质量检测、机器人视觉等。
更新日期/Last Update: 2023-05-23