Intelligent robots and key technologies for pharmaceutical production

被引：0
作者：
Wang K. [1 ,2 ]
Zhang H. [1 ,3 ]
Cao Y. [2 ,4 ]
Yi J. [1 ,2 ]
Yuan X. [1 ,2 ]
Wang Y. [1 ,2 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
[2] National Engineering Research Center of RVC, Hunan University, Changsha
[3] School of Robotics, Hunan University, Changsha
[4] College of Computer Science and Electronic Engineering, Hunan University, Changsha
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 07期
基金：
中国国家自然科学基金;
关键词：
canning and sealing; intelligent handling; intelligent robot; pharmaceutical dispensing; pharmaceutical production; quality inspection;
D O I：
10.13196/j.cims.2022.07.005
中图分类号：
学科分类号：
摘要：
The pharmaceutical industry is the foundation of the national economy and people's wellbeing, and the development of the pharmaceutical industry is of great significance to the national economy and the health of the citizens. With the development and maturity of artificial intelligence, intelligent robots have been widely used in the pharmaceutical production industry. Intelligent robots have enabled pharmaceutical production to achieve new breakthroughs in production efficiency and quality, but it also faces many problems and challenges. In view of this,the state-of-the-art research on intelligent robots in pharmaceutical production was reviewed. The common intelligent robots in pharmaceutical production and the challenges they faced in application were summarized, including aseptic pharmaceutical robots, filling and sealing robots,quality inspection robots, intelligent handling robots, etc. Aiming at the current challenges faced by intelligent robots for pharmaceutical production, key technologies such as control based on multi-source information perception, human-computer interaction,flexible grasping, bottle mouth positioning, quality inspection, path planning, and task scheduling were summarized. The future trend of intelligent robots for pharmaceutical production was prospected. © 2022 CIMS. All rights reserved.
引用
收藏
页码：1981 / 1995
页数：14
相关论文
共 68 条
[41]  
HUANG Senlin, WANG Yaonan, PENG Yu, Et al., Bottle mouth defect detection method based on hysteresis thresholding segmentation [J ], Journal of Electronic Measurement and Instrumentation, 31, 8, pp. 1289-1296, (2017)
[42]  
DU Xiaopeng, Comparison between automatic light inspection and manual light inspection[J], Mechanical and Electrical Information, 29, pp. 21-25, (2020)
[43]  
ZHANG Hui, WANG Yaonan, WU Chengzhong, Et al., Visual detection and recognition for medical infusion automatic production lines[J], Control Theory & Applications, 31, 10, pp. 1404-1413, (2014)
[44]  
DAI J Z., Drug residue detection system for infusion based on machine vision, Proceedings of International Conference on Smart City and Systems Engineering(ICSCSE), (2016)
[45]  
XIE Ziehen, Development status of several small-volume injection production equipment abroad[J], Mechanical and E-lectrical Information, 6, pp. 33-36, (2003)
[46]  
LIANG Mingliang, HUANG Meizhen, SUN Zhenhua, Et al., Analysis of photo-electronic signal characteristics and optical optimization for detection of impurities in ampoules[J], Journal of Shanghai Jiao Tong University, 48, 10, pp. 1357-1361, (2014)
[47]  
LI Jianle, ZHAO Liping, LIANG Yiwei, Bubble recognition method based on particle counter of light-blocking theory[J], Instrument Technique and Sensor, 9, pp. 29-32, (2018)
[48]  
FROSCH T, WYRWICH E, YAN D, Et al., Fiber-array-based raman hyperspectral imaging for simultaneous, chemi-cally-selective monitoring of particle size and shape of active ingredients in analgesic tablets [J], Molecules, 24, 23, (2019)
[49]  
NISHH T, MATSUZAKI K, MORITA S., Real-time determination and visualization of two independent quantities during a manufacturing process of pharmaceutical tablets by near-infrared hyperspectral imaging combined with multivariate analysis [ J ], International Journal of Pharmaceutics, 590, (2020)
[50]  
TCHOUNGA CAW, SACRE P Y, CIZA P, Et al., Composition analysis of falsified chloroquine phosphate samples seized during the COVID-19 pandemic[J], Journal of Pharmaceutical and Biomedical Analysis, 194, (2021)
1234567