一种基于深度学习的机械臂分拣方法

doi:10.11959/j.issn.2096-6652.202029

Abstract

Abstract:

A fast robot sorting method based on lightweight convolutional neural network was proposed to improve the recognition speed and environmental adaptability,especially for sorting complex objects.Firstly,the MobileNet-SSD algorithm was used to detect and classify the objects based on lightweight convolutional neural network.Secondly,image preprocessing and edge extraction were used to revise the object locations according to the above object detection results.The sorting experiments on PROBOT Anno robot arm show that the proposed method can achieve fast detection and location for complex objects.Compared with traditional image processing methods,the proposed method is robust to the diversity of target morphology and environment.

Key words: robot sorting, deep learning, object detection, machine vision

CLC Number:

TP242

Sijia TIAN, Qiang GU, Rong HU, et al. A robot sorting method based on deep learning[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 268-274.

Figures/Tables 11

References 11

[1]	LI C Y , MA Y B , WANG S Y ,et al. Novel industrial robot sorting technology based on machine vision[C]// International Conference on Modelling,Identification and Control. Piscataway:IEEE Press, 2017: 902-907.
[2]	HAZRAT ALI M , AIZAT K , YERHAN K ,et al. Vision-based robot manipulator for industrial applications[J]. Procedia Computer Science, 2018,133: 205-212.
[3]	WANG J W , DOU H Z , ZHENG S K ,et al. Target recognition based on machine vision for industrial sorting robot[J]. Journal of Robotics,Networking and Artificial Life, 2015,2(2): 100-102.
[4]	WANG S Y , LIN H , GAI R L ,et al. An application of vision technology on intelligent sorting system by delta robot[C]// IEEE International Conference on E-health Networking. Piscataway:IEEE Press, 2017.
[5]	HINTON G E , SALAKHUTDINOV R R . Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(5786): 504-507.
[6]	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 X D , CAI Y H , LU T ,et al. A robotic grasping method based on deep learning[J]. Robot, 2017,39(6): 820-828,837.
[7]	CHEN Z H , ZOU H B , WANG Y B ,et al. A vision-based robotic grasping system using deep learning for garbage sorting[C]// 2017 36th Chinese Control Conference. Piscataway:IEEE Press, 2017： 11223-11226
[8]	KUMAR R , KUMAR S , LAL S ,et al. Object detection and recognition for a pick and place robot[C]// Asia-pacific World Congress on Computer Science ＆ Engineering. Piscataway:IEEE Press, 2014: 1-7.
[9]	LIU W , ANGUELOV D , ERHAN D ,et al. SSD:single shot MultiBox detector[J]. European Conference on Computer Vision, 2016: 21-37.
[10]	LI Z M , PENG C , YU G ,et al. Light-head R-CNN:in defense of two-stage object detector[J]. arXiv preprint, 2017,arXiv:1711.07264.
[11]	HOWARD A G , ZHU M L , CHEN B ,et al. MobileNets:efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint, 2017,arXiv:1704.04861.

Metrics

Recommended 0

No Suggested Reading articles found!

类别	本文方法			基于Faster R-CNN的方法
类别	检测时间/s	正确检测个数/个	检测准确率	检测时间/s	正确检测个数/个	检测准确率
长颈鹿	0.078	293	97.7%	3.734	300	100%
鸭子	0.080	300	100%	3.721	300	100%
鹦鹉	0.082	290	96.7%	3.738	299	99.7%

类别	分拣实验次数/次	成功分拣次数/次	分拣成功率
长颈鹿	100	98	98%
鸭子	100	100	100%
鹦鹉	100	96	96%

[1]	Zhe HUANG, Yongcai WANG, Deying LI. A survey of 3D object detection algorithms [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 7-31.
[2]	Zhouyu GU, Yuecheng YU, Tiantian Zhe. Rapider-YOLOX: lightweight object detection network with high precision [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 92-103.
[3]	Jingwei LU, Xiang CHENG, Fei-Yue WANG. Artificial intelligence and deep learning methods for solving differential equations: the state of the art and prospects [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 461-476.
[4]	Jun ZHANG, Peidong XU, Siyuan CHEN, Tianlu GAO, Yuxin DAI, Ke ZHANG, Hang ZHAO, Jiemai GAO, Yuyang BAI, Jinxing LI, Haoran ZHANG, Xiang LI, Jiuxiang CHEN. A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 571-583.
[5]	Yan CHEN, Xueqin LUO, Wei LIANG, Yongfang XIE. Depression recognition based on emotional information fused with attentional mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 600-609.
[6]	Renwu LI, Lingxiao ZHANG, Lin GAO, Chunpeng LI, Hao JIANG. Category-level object pose estimation from depth point cloud [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 246-254.
[7]	Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN. Review of pedestrian trajectory prediction methods [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(4): 399-411.
[8]	Ying LI, Long CHEN, Zhaohong HUANG, Yang SUN, Guorong CAI. Plant leaf detection technology based on multi-scale CNN feature fusion [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 304-311.
[9]	Qing TIAN, Rong HU, Zuoyong LI, Yuanzheng CAI, Zhaochai YU. Insulator detection based on SE-YOLOv5s [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 312-321.
[10]	Wen LIU, Kunlin HU, Yan LI, Zhao LIU. A review of prediction methods for moving target trajectories [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 149-160.
[11]	Yang ZHANG, Yue HU, Dongrong XIN. A deep learning short-term traffic flow prediction method considering spatial-temporal association [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 172-178.
[12]	Dongjun LIU, Yuhan WANG, Wenfen LING, Yong PENG, Wanzeng KONG. Emotion recognition based on brain and machine collaborative intelligence [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(1): 65-75.
[13]	Liang ZHAO, Zhifeng XIE, Kunpeng ZHANG, Yuqing ZHENG, Yuankun FU. Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 401-411.
[14]	Fei-Yue WANG,Dongpu CAO,Qinglai WEI. Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 101-106.
[15]	Xiaofeng YUAN,Yalin WANG,Chunhua YANG,Weihua GUI. The application of deep learning in data-driven modeling of process industries [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 107-115.

A robot sorting method based on deep learning

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 11

Related Articles 15

Metrics

Recommended 0