|
基于机器视觉的桁架机器人自动上下料系统的设计
|
Abstract:
针对电力系统电缆保护管自动垂直仓储问题,研制了基于机器视觉的桁架机器人自动上下料系统。首先设计了由桁架机器人本体、视觉导引系统、机器人运动控制系统三部分组成的自动上下料系统,介绍了机器人本体结构,系统控制策略;其次在搭建了系统的手眼定位系统的基础上,详细介绍了深度可分类卷积神经网络分类识别模型;最后通过系统的现场测试和验证。实验结果表明深度可分离卷积神经网络识别模型的符合设计预期,实现了待测物体的图像分割,分类和定位。桁架机器人自动上下料系统,符合工程应用要求,实现了高效,稳定的自动上下料功能。
According to the automatic vertical storage problem of the power system cable protection pipe, the automatic feeding system of the truss robot based on machine vision is developed. Firstly, the automatic loading and unloading system composed of the truss robot body, the robot guidance system and the robot motion control system is introduced. Secondly, the model is introduced. Finally, the field test and verification of the system are passed. The experimental results of the clear and deep separable convolutional neural network recognition model meet the design expectation, and realizes the image segmentation, classification and positioning of the objects to be measured. The truss robot automatic loading and unloading system meets the requirements of engineering application and realizes the efficient and stable automatic loading and unloading function.
[1] | 宋春华, 彭法知. 机器视觉研究与发展综述[J]. 装备制造技术, 2019(6): 213-216. |
[2] | Wan, S. and Goudos, S. (2019) Faster R-CNN for Multi-Class Fruit Detection Using a Robotic Vision System. Computer Networks, 168, 107036. https://doi.org/10.1016/j.comnet.2019.107036 |
[3] | 日本FANUC公司. FANUC智能化机床上下料[J]. 伺服控制, 2014(1): 113-117. |
[4] | 张磊. 基于机器视觉的机器人自动上下料系统[D]: [硕士学位论文]. 上海: 上海交通大学, 2018. |
[5] | 周书华, 雷伟敏, 叶晓平, 等. 基于视觉导引的工业机器人动态分拣与控制系统开发[J]. 丽水学院学报, 2019, 41(5): 10-15. |
[6] | 陈彦峰. 基于机器视觉的轮毂上下料机器人工作站开发[D]: [硕士学位论文]. 金华: 浙江师范大学, 2021. |
[7] | 程子华. 基于机器视觉的残缺饼干分拣系统开发[J]. 现代食品技术, 2022, 38(2): 313-318, 325. |
[8] | 程麒. 龙门架式焊接机器人立体视觉系统设计[D]: [硕士学位论文]. 无锡: 江南大学, 2021. |
[9] | Sifre, L. and Mallat, S. (2013) Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, 23-28 June 2013, 1233-1240.
https://doi.org/10.1109/CVPR.2013.163 |
[10] | 刘恒畅, 姚德臣, 杨建伟, 张骄. 基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究[J]. 振动与冲击, 2021, 40(10): 95-102. |
[11] | 邓若辰, 彭程, 边赟. 基于深度可分离卷积的指静脉识别算法[J]. 计算机应用, 2020, 40(z2): 199-203. |