Hostname: page-component-77c89778f8-9q27g Total loading time: 0 Render date: 2024-07-17T10:05:44.817Z Has data issue: false hasContentIssue false
  • English
  • Français

A Knowledge-Based Approach to Modelling of Robotic Assembly Cells

Published online by Cambridge University Press:  09 March 2009

Dae-Won Kim ,
Bum-Hee Lee  and
Myoung-Sam Ko
Dae-Won Kim
Affiliation:
Robotics and Intelligent Systems Laboratory, Department of Control and Instrumentation Engineering, Seoul National University, Seoul 151–742 (Korea)
Bum-Hee Lee
Affiliation:
Robotics and Intelligent Systems Laboratory, Department of Control and Instrumentation Engineering, Seoul National University, Seoul 151–742 (Korea)
Myoung-Sam Ko
Affiliation:
Robotics and Intelligent Systems Laboratory, Department of Control and Instrumentation Engineering, Seoul National University, Seoul 151–742 (Korea)
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, an approach to modelling of a robotic assembly cell is proposed and a method for managing the cell operation is described using a knowledge base. Since the modelling structure is based on the concept of the state variable, the relationships between states are described by the state transition map (STM). The knowledge-bases for state transition and assembly job information are obtained from the STM and the assembly job tree (AJT), respectively. Using the knowledge-base, the System structure is discussed in relation to both managing the cell operation and evaluating the performances. Finally, a simulation algorithm is presented with the simulation results to show the significance of the proposed modelling approach.

Keywords

ModellingAssembly cellsRoboticsKnowledge-baseState transition
Type
Article
Information
Robotica , Volume 9 , Issue 1 , January 1991 , pp. 31 - 42
Copyright
Copyright © Cambridge University Press 1991

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Kusiak, Andrew, “Flexible manufacturing Systems: a structural approach” Int. J. Prod. Res. 23, No. 6, 1057–1073 (1985).Google Scholar
2.Ho, Y.C., “Scanning the Issue”, Proc. of the IEEE 3–6 (01, 1989).CrossRefGoogle Scholar
3.Rathmill, K., Robotic Assembly (IFS, Bedford, UK, 1985).Google Scholar
4.Yao, David D. and Buzacott, J.A., “Modelling the performance of flexible manufacturing Systems” Int. J. Prod. Res. 23, No. 5, 945–959 (1985).CrossRefGoogle Scholar
5.Garzia, Ricardo F. et al., “Discrete-Event Simulation” IEEE Spectrum 32–36 (1986).CrossRefGoogle Scholar
6.Ho, Y.C., “Performance Evaluation and Perturbation Analysis of Discrete Event Dynamic Systems” IEEE Trans. Automat. Contr. AC-32, 563–572 (1987).Google Scholar
7.Aveyard, Robert L., “A Boolean model for a class of discrete event System” IEEE Trans. on System, Man and Cybernetics SMC-4, No. 3 (05 1974).Google Scholar
8.Peterson, James L., Petri Net Theory and the Modeling of Systems (Prentice-Hall, New York, 1981).Google Scholar
9.Reisig, Wolfgang, Petri Nets (Springer-Verlag, Berlin. 1985).CrossRefGoogle Scholar
10.Kamath, M. and Viswanadham, N., “Application of Petri net based models in the modelling and analysis of flexible manufacturing Systems” Proc. of the 1986 IEEE of Int. Conf. on Robotics and Automation 312–317 (1986).Google Scholar
11.Viswanadham, N. and Narahari, Y., “Colored Petri net models for automated manufacturing Systems” Proc. of the 1987 IEEE Int. Conf. on Robotics and Automation 1985–1990 (1987).Google Scholar
12.Beck, Carolyn L. and Krogh, Bruce H., “Models for simulation and discrete control of manufacturing Systems” Proc. of the 1986 IEEE Int. Conf. on Robotics and Automation 305–310 (1986).Google Scholar
13.Ostroff, J.S. and Wonham, W.M., “State Machines, Temporal Logic and Control: a Framework for Discrete Event Systems” Proc. of the 28th Conf. on Decision and Control 681–686 (1987).CrossRefGoogle Scholar
14.Golaszewski, C.H. and Ramadge, P.J., “Control of discrete event processes with forced events” Proc. of the 26th Conf. on Decision and Control 247–251 (12, 1987).CrossRefGoogle Scholar
15.Goos, G. and Hartmanis, J., Net Theory and Applications (Lecture Notes in Computer Science) 84 (Springer-Verlag, Berlin, 1979).Google Scholar
16.Kim, Steven H. “An Automata—Theoretic Framework for Intelligent Systems” Robotics and Computer-Integrated Manufacturing 5, No. 1, 43–51 (1989).CrossRefGoogle Scholar
17.Maimon, O.Z. and Nof, S.Y., “Coordination of Robots Sharing Assembly Tasks” Tran. of the ASME, Journ. of Dynamic Systems, Measurement and Control 107, 299–307 (12, 1985).Google Scholar
18.Staroswiecki, M., Djeghaba, M. and Bayart, M., “Tasks scheduling by multicriteria optimization in a flexible assembly cell using robot cooperation” Proc. of 15th ISIR 847–854, Tokyo, Japan (1985).Google Scholar
19.Ben-Arien, David H., Moodie, Colin L. and Chu, Chi-Chung, “Control Methodology for FMS” IEEE Journ. of Robotics and Automation 4, No. 1, 53–59 (02, 1988).CrossRefGoogle Scholar
20.Kusiak, A. and Villa, A., “Architectures of expert Systems for scheduling flexible manufacturing Systems” Proc. of the 1987 IEEE Int. Conf. on Robotics and Automation 113–117 (1987).Google Scholar
21.Kak, A.C., Boyer, K.L., Chen, C.H., Safranek, R.J. and Yang, H.S., “A Knowledge-Based Robotic Assembly Cell” IEEE Expert 63–83 (Spring, 1986).CrossRefGoogle Scholar
22.Ben-Arien, David et al., “Knowledge-Based Control System for Automated Production and Assembly” Towards the Factory of the Future (Springer-Verlag, Berlin, 1985).Google Scholar
23.Hawker, J.S. and Nagel, R.N., “World Models in Intelligent Control Systems” Proc. of the 1987 IEEE Conf. on Intelligent Control 482–488 (1987).Google Scholar
24. “Challenges to Control: A Collective View” Editorial IEEE Trans. Automat. Contr. AC-32, 275–285 (1987).CrossRefGoogle Scholar
25.Zeigler, Bernard P., Theory of Modelling and Simulation (John Wiley & Sons, New York, 1976).Google Scholar
26.Frost, R.A., Introduction to Knowledge Base Systems (Collins, London, 1986).Google Scholar
27.Hunt, V. Daniel, Artificial Intelligence and Expert Systems Source Book (Chapman & Hall, London 1986).CrossRefGoogle Scholar
28.Golden Common LISP (Gold Hill Computers, Inc., 1983).Google Scholar
29.Levi, Paul and Loeffler, Thomas, “The Use of Assembly Graphs for Robot Programming” Languages for Sensor-Based Control in Robotics (Springer-Verlag, Berlin, 1987) pp. 233–259.Google Scholar
30.Fox, B.R. and Ho, C.Y., “A relational control mechanism for flexible assembly” Advanced Software in Robotics 43–53 (North-Holland, Amsterdam, 1984).Google Scholar
31.Kim, Dae-Won, Ko, Myoung-Sam and Lee, Bum-Hee, “A Framework for Modelling and Operation Management of Robotic Assembly Cells via Knowledge Base” Proc. of the 1988 KACC 1, 374–379, Seoul, Korea (1988).Google Scholar
32.Shapiro, Stuart C. et al., Encylopedia of Artificial Intelligence (Wiley-Interscience, New York, 1987).Google Scholar
33.Sell, Peter S., Expert Systems – A Practical Introduction (John Wiley and Sons, Chichester, UK, 1985).CrossRefGoogle Scholar