Please wait a minute...

Frontiers of Mechanical Engineering

Front. Mech. Eng.    2020, Vol. 15 Issue (1) : 1-11
Towards a next-generation production system for industrial robots: A CPS-based hybrid architecture for smart assembly shop floors with closed-loop dynamic cyber physical interactions
Qingmeng TAN, Yifei TONG(), Shaofeng WU, Dongbo LI
School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
Download: PDF(992 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Given the multiple varieties and small batches, the production of industrial robots faces the ongoing challenges of flexibility, self-organization, self-configuration, and other “smart” requirements. Recently, cyber physical systems have provided a promising solution for the requirements mentioned above. Despite recent progress, some critical issues have not been fully addressed at the shop floor level, including dynamic reorganization and reconfiguration, ubiquitous networking, and time constrained computing. Toward the next generation production system for industrial robots, this study proposed a hybrid architecture for smart assembly shop floors with closed-loop dynamic cyber physical interactions. Aiming for dynamic reorganization and reconfiguration, the study also proposed modularized smart assembly units for the deployment of physical assembly processes. Enabling technologies, such as multiagent system (MAS), self-organized wireless sensor actuator networks, and edge computing, were discussed and then integrated into the proposed architecture. Furthermore, a multijoint robot assembly process was selected as a target scenario. Thus, an MAS was developed to simulate the coordination and negotiation mechanisms for the proposed architecture on the basis of the Java Agent Development Framework platform.

Keywords cyber physical system      robot assembly      multiagent system      architecture     
Corresponding Author(s): Yifei TONG   
Just Accepted Date: 27 December 2019   Online First Date: 20 January 2020    Issue Date: 21 February 2020
 Cite this article:   
Qingmeng TAN,Yifei TONG,Shaofeng WU, et al. Towards a next-generation production system for industrial robots: A CPS-based hybrid architecture for smart assembly shop floors with closed-loop dynamic cyber physical interactions[J]. Front. Mech. Eng., 2020, 15(1): 1-11.
Fig.1  Hybrid architecture of CPS based smart assembly shop floors.
Fig.2  Prototype of smart assembly unit (SAU).
Node type Functionality Mobility Routing mode Instances
Sensor node Sense Yes Multi-hop Temperature/humidity sensors
Actuator node Actuate Yes Multi-hop Pneumatic actuators
Hybrid node Both sense and actuate Some yes Multi-hop SAUs/AGVs
Tab.1  Node types in WSAN
Fig.3  MAS framework.
Fig.4  Main assembly process of multijoint robots.
Fig.5  Simulation layout of arm component assembly shop floors.
Fig.6  ACL message communication between physical agents in distributed JADE. DF: Directory facilitator; AMS: Agent management system.
Fig.7  Communication sequence diagram of GPM.
Fig.8  Communication sequence diagram of LNM.
Agent name Encapsulation entity Types
MCA Mechanical components assembly Coordination agent
SAU1 No. 1 SAU for arm assembly Physical agent
SAU2 No. 2 SAU for arm assembly Physical agent
SAU3 No. 3 SAU for gear assembly Physical agent
AGV AGV for transportation Physical agent
Tab.2  Agent descriptions
Random time sa/(m?min–1) d(1)/m Tsc(1)/min Tsp(1)/min Tsw(1)/min d(2)/m Tsc(2)/min Tsp(2)/min Tsw(2)/min d(3)/m Tsc(3)/min Tsp(3)/min Tsw(3)/min
ta 20 20 1 4 5 40 2 6 8 60 3 5 1
tb 20 20 1 4 1 0 0 6 2 20 1 5 2
Tab.3  Parameters in random time ta and tb
Random time Ts(1)/min Ts(2)/min Ts(3)/min min( Ts)/min Contracted SAU
ta 10 16 9 9 SAU3
tb 6 8 8 6 SAU1
Tab.4  Coordination results
1 IFR forecast: 1.7 million new robots to transform the world’s factories by 2020. Available at International Federation of Robotics website on September 27, 2017
2 L Monostori, B Kádár, T, Bauernhansl et al. Cyber-physical systems in manufacturing. CIRP Annals, 2016, 65(2): 621–641
3 J Lee, B Bagheri, H Kao. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 2015, 3: 18–23
4 S Wang, J Wan, D Zhang, et al. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 2016, 101: 158–168
5 C Liu, P Jiang. A cyber-physical system architecture in shop floor for intelligent manufacturing. Procedia CIRP, 2016, 56: 372–377
6 K Thramboulidis, D C Vachtsevanou, A Solanos. Cyber-physical microservices: An IoT-based framework for manufacturing systems. 2018 IEEE Industrial Cyber-Physical Systems (ICPS), 2018, 232–239
7 F Lu. The ZigBee based wireless sensor and actor network in intelligent space oriented to home service robot. International Journal of Communications, Network and System Sciences, 2012, 5(5): 280–285
8 G Horvat, D Zagar, J Vlaovic. Evaluation of quality of service provisioning in large-scale pervasive and smart collaborative wireless sensor and actor networks. Advanced Engineering Informatics, 2017, 33: 258–273
9 V K Manupati, G D Putnik, M K Tiwari, et al. Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment. Computers & Industrial Engineering, 2016, 94: 63–73
10 A V Barenji, R V Barenji, M Hashemipour. Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Advances in Engineering Software, 2016, 91: 1–11
11 R Cupek, A Ziebinski, L Huczala, et al.Agent-based manufacturing execution systems for short-series production scheduling. Computers in Industry, 2016, 82: 245–258
12 W Shen, Q Hao, H J Yoon, et al.Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, 2006, 20(4): 415–431
13 S Giordani, M Lujak, F Martinelli. A distributed multi-agent production planning and scheduling framework for mobile robots. Computers & Industrial Engineering, 2013, 64(1): 19–30
14 N He, D Z Zhang, Q Li. Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. International Journal of Production Economics, 2014, 149: 117–130
15 H E Nouri, O Belkahla Driss, K Ghédira. Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model. Computers & Industrial Engineering, 2016, 102: 488–501
16 P Leitão, S Karnouskos, L Ribeiro, et al. Smart agents in industrial cyber-physical systems. Proceedings of the IEEE, 2016, 104(5): 1086–1101
17 R van der Meulen. Gartner says 8.4 billion connected “Things” will be in use in 2017, up 31 percent from 2016. Available at Gartner website on February 7, 2017
18 M Satyanarayanan. The emergence of edge computing. Computer, 2017, 50(1): 30–39
19 Y Ai, M Peng, K Zhang. Edge cloud computing technologies for Internet of Things: A primer. Digital Communications and Networks, 2018, 4(2): 77–86
Related articles from Frontiers Journals
[1] Zheng-Dong MA. Macro-architectured cellular materials: Properties, characteristic modes, and prediction methods[J]. Front. Mech. Eng., 2018, 13(3): 442-459.
[2] Hongxue XU, Hong ZHENG, Xiuying GUO. Modular architecture model of CSCD system[J]. Front Mech Eng Chin, 2010, 5(4): 470-475.
[3] Xiaogen NIE, Yanbing LIU. NC flame pipe cutting machine tool based on open architecture CNC system[J]. Front Mech Eng Chin, 2009, 4(2): 147-152.
[4] CHU Hong-yan, CAO Quan-jun, FEI Ren-yuan. MAS-based production scheduling system for manufacturing cell-based workshop[J]. Front. Mech. Eng., 2006, 1(4): 375-380.
Full text



  Shared   0