Full Text:   <2843>

Summary:  <204>

Suppl. Mater.: 

CLC number: TP24

On-line Access: 2023-07-03

Received: 2021-09-04

Revision Accepted: 2022-05-10

Crosschecked: 2023-07-03

Cited: 0

Clicked: 1542

Citations:  Bibtex RefMan EndNote GB/T7714


Da-peng Tan


Huanpei LYU


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.6 P.890-905


A collaborative assembly for low-voltage electrical apparatuses

Author(s):  Huanpei LYU, Libin ZHANG, Dapeng TAN, Fang XU

Affiliation(s):  College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China; more

Corresponding email(s):   lvhuanpei@nbufe.edu.cn, lbz@zjut.edu.cn, tandapeng@zjut.edu.cn, fangx@zjut.edu.cn

Key Words:  Low-voltage electrical apparatus, Collaborative assembly, Artificial potential field based planning, Adaptive quantum genetic algorithm, Dynamic interaction

Huanpei LYU, Libin ZHANG, Dapeng TAN, Fang XU. A collaborative assembly for low-voltage electrical apparatuses[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 890-905.

@article{title="A collaborative assembly for low-voltage electrical apparatuses",
author="Huanpei LYU, Libin ZHANG, Dapeng TAN, Fang XU",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A collaborative assembly for low-voltage electrical apparatuses
%A Huanpei LYU
%A Libin ZHANG
%A Dapeng TAN
%A Fang XU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 6
%P 890-905
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100423

T1 - A collaborative assembly for low-voltage electrical apparatuses
A1 - Huanpei LYU
A1 - Libin ZHANG
A1 - Dapeng TAN
A1 - Fang XU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 6
SP - 890
EP - 905
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100423

low-voltage electrical apparatuses (LVEAs) have many workpieces and intricate geometric structures, and the assembly process is rigid and labor-intensive, and has little balance. The assembly process cannot readily adapt to changes in assembly situations. To address these issues, a collaborative assembly is proposed. Based on the requirements of collaborative assembly, a colored Petri net (CPN) model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly. Also, an artificial potential field based planning algorithm (AFPA) is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs. Then an adaptive quantum genetic algorithm (AQGA) is developed to optimize the assembly process. Lastly, taking a two-pole circuit-breaker controller with leakage protection (TPCLP) as an assembly instance, comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly. The distribution of resources can also be optimized in the assembly. The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.


摘要:低压电器设备由较多零部件组成,结构较为复杂,其现有装配方法多是刚性、劳动密集和低平衡的装配工艺过程,不能随装配环境变化迅速改变。本文提出一种面向低压电器的协同装配方法。首先,根据协同装配的性能要求,构建着色Petri网模型,以分析协同装配中各机器人的自治性能和交互特性。其次,在装配控制中提出一种基于规划的人工势场算法(artificial potential fieldbased planning algorithm, AFPA),以实现低压电器设备协同装配中机器人静态全局规划和动态交互控制,并引入自适应量子遗传算法(adaptive quantum genetic algorithm, AQGA)对整个装配过程进行平衡优化。最后,以带漏电保护装置的二相断路器为例,对协同装配方法进行模拟分析。结果表明,低压电器装配中,协同装配方法具有较好的成本效益和柔性,同时装配资源得到较好分配。装配机器人能够相互间动态交互以适应低压电器设备装配中的变化。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]André É, Benmoussa MM, Choppy C, 2016. Formalising concurrent UML state machines using coloured Petri nets. Form Aspects Comput, 28(5):805-845.

[2]Battaïa O, Dolgui A, 2013. A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ, 142(2):259-277.

[3]Baybars İ, 1986. A survey of exact algorithms for the simple assembly line balancing problem. Manag Sci, 32(8):909-932.

[4]Baykasoglu A, 2006. Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. J Intell Manuf, 17(2):217-232.

[5]Charbonnier F, Alla H, David R, 1999. Discrete-event dynamic systems. IEEE Trans Contr Syst Technol, 7(2):175-187.

[6]Chen ST, Tan DP, 2018. A SA-ANN-based modeling method for human cognition mechanism and the PSACO cognition algorithm. Complexity, 2018:6264124.

[7]Çil ZA, Mete S, Özceylan E, et al., 2017. A beam search approach for solving type II robotic parallel assembly line balancing problem. Appl Soft Comput, 61:129-138.

[8]Deb K, 1998. Genetic algorithm in search and optimization: the technique and applications. Proc Int Workshop on Soft Computing and Intelligent Systems, p.58-87.

[9]Desel J, Reisig W, 1998. Place/transition Petri nets. Proc Advanced Course on Petri Nets, p.122-173.

[10]Gao J, Sun LY, Wang LH, et al., 2009. An efficient approach for type II robotic assembly line balancing problems. Comput Ind Eng, 56(3):1065-1080.

[11]Ge M, Ji SM, Tan DP, et al., 2021. Erosion analysis and experimental research of gas-liquid-solid soft abrasive flow polishing based on cavitation effects. Int J Adv Manuf Technol, 114(11-12):3419-3436.

[12]Grzechca W, 2014. Assembly line balancing problem with reduced number of workstations. IFAC Proc Vol, 47(3):6180-6185.

[13]Jensen K, 1990. Coloured petri nets: a high level language for system design and analysis. Proc Int Conf on Application and Theory of Petri Nets, p.342-416.

[14]Ji SM, Weng XX, Tan DP, 2012. Analytical method of softness abrasive two-phase flow field based on 2D model of LSM. Acta Phys Sin, 61(1):010205(in Chinese).

[15]Johannsmeier L, Haddadin S, 2017. A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot Autom Lett, 2(1):41-48.

[16]Khatib O, 1986. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Resh, 5(1):90-98.

[17]Levitin G, Rubinovitz J, Shnits B, 2006. A genetic algorithm for robotic assembly line balancing. Eur J Oper Res, 168(3):811-825.

[18]Li DL, 2004. Electrical Control & the Principle and Application of PLC. Publishing House of Electronics Industry, Beijing, China (in Chinese).

[19]Li L, Lu JF, Fang H, et al., 2020. Lattice Boltzmann method for fluid-thermal systems: status, hotspots, trends and outlook. IEEE Access, 8:27649-27675.

[20]Li L, Tan DP, Yin ZC, et al., 2021. Investigation on the multiphase vortex and its fluid-solid vibration characters for sustainability production. Renew Energy, 175:887-909.

[21]Li SY, Li PC, 2006. Quantum genetic algorithm based on real encoding and gradient information of object function. J Harbin Inst Technol, 38(8):1216-1218, 1223(in Chinese).

[22]Li XL, Xing KY, Lu QC, 2021. Hybrid particle swarm optimization algorithm for scheduling flexible assembly systems with blocking and deadlock constraints. Eng Appl Artif Intell, 105:104411.

[23]Montiel O, Sepúlveda R, Orozco-Rosas U, 2015. Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Robot Syst, 79(2):237-257.

[24]New S, 1994. Modeling and analysis of manufacturing systems. J Oper Res Soc, 45(6):725-726.

[25]Nilakantan JM, Ponnambalam SG, Jawahar N, et al., 2015. Bio-inspired search algorithms to solve robotic assembly line balancing problems. Neur Comput Appl, 26(6):‍1379-1393.

[26]Özcan U, Toklu B, 2009. A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems. J Intell Manuf, 20(1):123-136.

[27]Pan Y, Ji SM, Tan DP, et al., 2020. Cavitation-based soft abrasive flow processing method. Int J Adv Manuf Technol, 109(9):2587-2602.

[28]Ren CX, Zhang H, Fan YZ, 2015. Optimizing dispatching of public transit vehicles using genetic simulated annealing algorithm. J Syst Simul, 17(9):2075-2077, 2081(in Chinese).

[29]Rizwan M, Patoglu V, Erdem E, 2020. Human robot collaborative assembly planning: an answer set programming approach. Theory Pract Log Program, 20(6):1006-1020.

[30]Rubinovitz J, Bukchin J, Lenz E, 1993. RALB: a heuristic algorithm for design and balancing of robotic assembly lines. CIRP Ann, 42(1):497-500.

[31]Samouei P, Fattahi P, Ashayeri J, et al., 2016. Bottleneck easing-based assignment of work and product mixture determination: fuzzy assembly line balancing approach. Appl Math Modell, 40(7-8):4323-4340.

[32]Tan DP, Chen ST, Bao GJ, et al., 2018. An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring. Front Inform Technol Electron Eng, 19(5):604-625.

[33]Tavakoli A, 2020. Multi-criteria optimization of multi product assembly line using hybrid tabu-SA algorithm. SN Appl Sci, 2(2):151.

[34]Wang H, Chen Z, Huang JH, et al., 2022. Development of high-speed on‍–‍off valves and their applications. Chin J Mech Eng, 35(1):67.

[35]Wang JX, Gao SB, Tang ZJ, et al., 2023. A context-aware recommendation system for improving manufacturing process modeling. J Intell Manuf, 34:1347-1368.

[36]Wang YY, Zhang YL, Tan DP, et al., 2021. Key technologies and development trends in advanced intelligent sawing equipments. Chin J Mech Eng, 34(1):30.

[37]Xie N, 2006. Research on Modeling, Scheduling and Controller of Reconfigurable Manufacturing System Using Petri Nets. PhD Thesis, Tongji University, Shanghai, China(in Chinese).

[38]Yang JN, Li B, Zhang ZQ, 2003. Research of quantum genetic algorith and its application in blind source separation. J Electron, 20(1):62-68.

[39]Yu MY, Yang JJ, 2018. Research on flexible assembly system for multi-variety and small-batch products. Mech Eng Autom, (6):39-41(in Chinese).

[40]Zelenka J, 2010. Discrete event dynamic systems framework for analysis and modeling of real manufacturing system. Proc 14th Int Conf on Intelligent Engineering System, p.287-291.

[41]Zeng X, Ji SM, Jin MS, et al., 2014. Investigation on machining characteristic of pneumatic wheel based on softness consolidation abrasives. Int J Prec Eng Manuf, 15(10):2031-2039.

[42]Zhang JY, Liu T, 2007. Optimized path planning of mobile robot based on artificial potential field. Acta Aeronaut Astronaut Sin, 28(S1):S183-S188(in Chinese).

[43]Zhang K, Zhang JH, Gan MY, et al., 2022. Modeling and parameter sensitivity analysis of valve-controlled helical hydraulic rotary actuator system. Chin J Mech Eng, 35(1):66.

[44]Zheng SH, Yu YK, Qiu MZ, et al., 2021. A modal analysis of vibration response of a cracked fluid-filled cylindrical shell. Appl Math Modell, 91:934-958.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE