Generation of optimal operating strategies for robotic cells: a Petri net approach

Abstract This paper presents a methodology for generating optimal operating strategics for robotic cells. The methodology is built on two techniques: stochastic Petri net and Markov decision process. The operating strategy devised with the proposed methodology is deadlock-free and provides the best performance given a set of systems parameters (machines, parts, machining times, transportation times and so on). Generation of programmable logic controller (PLC) codes is greatly facilitated as the generated operating strategy can be mapped directly to n ladder diagram representation of the PLC code. In this paper, both the stochastic Petri net and Markov decision process techniques are discussed. An example is provided to illustrate the methodology.

[1]  Miryam Barad,et al.  Flexibility in manufacturing systems: definitions and Petri net modelling , 1988 .

[2]  Giovanni Chiola,et al.  Generalized stochastic Petri nets for the performance evaluation of FMS , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[3]  Zoran Ognjanovic,et al.  An approach to the modeling of the highest control level of flexible manufacturing cell , 1990, Robotica.

[4]  Bruce H. Krogh,et al.  Essentially decision free Petri nets for real-time resource allocation , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[5]  P. David Stotts,et al.  Modelling the logical structure of flexible manufacturing systems with Petri-nets , 1989, Comput. Commun..

[6]  Paul J. Schweitzer,et al.  On the existence of relative values for undiscounted Markovian decision processes with a scalar gain rate , 1984 .

[7]  Maurizio Morisio,et al.  Petri-net based simulation of manufacturing cells , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[8]  Nicholas Zvegintzov.  Structured representation of FMS integrating SI-NETS and high level petri nets , 1990 .

[9]  Hsu-Pin Ben Wang,et al.  Conflict detection of automated guided vehicles: a Petri net approach , 1991 .

[10]  Henk Tijms,et al.  Stochastic modelling and analysis: a computational approach , 1986 .

[11]  H.E. Garnousset,et al.  Efficient tools for analysis and implementation of manufacturing systems modelled by Petri net with objects: a production rules compilation-based approach , 1989, 15th Annual Conference of IEEE Industrial Electronics Society.

[12]  A. Hormann.  A Petri net based control architecture for a multi-robot system , 1989, Proceedings. IEEE International Symposium on Intelligent Control 1989.

[13]  Thomas O. Boucher,et al.  Petri net control of an automated manufacturing cell , 1989 .

[14]  Manuel Silva Suárez,et al.  Modeling, validation and software implementation of production systems using high level Petri nets , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[15]  J. C. Gentina,et al.  Coloured adaptive structured petri-net a tool for the automatic synthesis of hierarchical control of flexible manufacturing systems , 1988 .

[16]  Marc Courvoisier,et al.  Joining Petri nets and knowledge based systems for monitoring purposes , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[17]  Itsuo Hatono,et al.  Modeling of FMS under certainty using stochastic Petri nets-an application to rule-based on-line scheduling , 1989, Proceedings of the Third International Workshop on Petri Nets and Performance Models, PNPM89.

[18]  Itsuo Hatono,et al.  Modeling and On-Line Scheduling of Flexible Manufacturing Systems Using Stochastic Petri Nets , 1991, IEEE Trans. Software Eng..

[19]  D. White,et al.  Dynamic programming, Markov chains, and the method of successive approximations , 1963 .

[20]  Qun Jin,et al.  Probabilistic behavior and reliability analysis for a multi-robot system by applying Petri net and Markov renewal process theory , 1989 .