**7. References**


[4] Lozano-Perez, T. (1983). Robot Programming. *Proceedings of the IEEE*, Vol. 71, No. 7, pp. 821-841

92 Petri Nets – Manufacturing and Computer Science

**6. Conclusions** 

**Author details** 

Gen'ichi Yasuda

**7. References** 

on the organization level in multiple robot systems.

*Magazine*, IEEE, Vol. 14, No. 12, pp. 82-96

*Nagasaki Institute of Applied Science, Japan* 

The multi-robot controller accomplishes the specified task by executing the net model constructed above. Unit actions in a net model used for lowest level controllers are defined in a specified task space, where the action is executed. In the control software the position and orientation in the task space is transformed to the robot coordinate system using the homogeneous transformation matrix. The controller coordinates and supervises the individual controllers based on information explicitly represented by places, place parameters, transitions, arcs, and gates. That is, when a token enters a place that represents a subtask, immediately the controller defined by the control code is informed to execute the subtask with a specified data. Because of the proper nature of the Petri net, the designer can easily create a multi-robot task program which is free of logical errors. The method acts as a programming method on the coordination level and on the organization level [20]. That is, the Petri net is applied as a tool to the operator who plans the multi-robot task, and by executing the net model the individual hardware controllers are regulated and supervised. If, before moving the real robot, the outputs of the robot controller are linked with the graphic robot motion simulator, the whole task programmed can be tested off-line. When the task specification is required to be changed, the net model can be modified on-line.

It was confirmed that the multi-robot controller developed based on tasks programmed in the net form controls the equipment according to the programmed net model. The method provides concurrent movement of all robots and machines in the system, and it provides synchronization commands to allow coordination of their movements to accomplish user defined tasks. The commands used by this system are not based on any specific existing robot language. So, the method can be used in any real robot by translating it to the appropriate robot language, and it acts as a programming tool on the coordination level and

[1] Martinez, J., Muro, P. & Silva, M. (1987). Modeling, Validation and Software Implementation of Production Systems Using High Level Petri Nets, *Proceedings of IEEE* 

[2] Sakane, S. (1993). Distributed Sensing System with 3D model-based agents, *Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems*, pp. 1157-1163 [3] Bonner, J. & Shin, K. G. (1982). A Comparative Study of Robot Languages. *Computer* 

*International Conference on Robotics and Automation*,pp. 1180-1185

	- [19] Yasuda, G. & Tachibana, K. (1991). A Parallel Processing Control Approach to the Design of Autonomous Robotic Manufacturing Systems, *Proceedings of the XIth International Conference on Production Research*, pp. 445-449

**Chapter 5** 

© 2012 Facchin and Sellitto, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Facchin and Sellitto, licensee InTech. This is a paper distributed under the terms of the Creative Commons

**Measurement of Work-in-Process** 

**by Petri Nets Modeling and Throughput Diagram** 

A proper planning and the search for better results in the production processes are important for the competitiveness that manufacturing can add to business operations. However, changes in manufacturing involve risks and uncertainties that may affect the company's operations. In this case, modeling and simulation of the production line can assist the decision-making process, avoiding unnecessary expenses and risks before making a decision. A model that can be simulated in the computer is a mechanism that turns input parameters, known and associated requirements of the process, into output parameters and performance metrics that have not yet happened in the real world (Law; Kelton, 1991).

Thereby, a line production model, which can be used in a computer simulation, can be a tool for decision support, because, before the results will crystallize in the real world

Inventory in process and throughput time that a production plan will generate are quantities that may be useful in decision making in manufacturing and can be predicted by computer simulation. The inventory process (work in process or WIP) consists of materials that have already been released for manufacture (have already left the warehouse or have been received from suppliers), but their orders still not been completed. Lead time is the time between release manufacture order and the product availability for shipment to the customer (Antunes et al., 2007). Some decisions in internal logistics of manufacturing may be related to these quantities: choosing alternatives for compliance with scheduled delivery dates, intermediate storage areas for processing of applications, equipment for internal movement; resources for tool changes and machinery preparation. The most important decision that can be supported by the proposed method is the definition of in-process

manufacturing, it can be predicted, with a given reliability, in virtual simulation.

**and Manufacturing Lead Time** 

Tiago Facchin and Miguel Afonso Sellitto

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50288

**1. Introduction** 

[20] Graham, J. H. & Saridis, G. N. (1982). Linguistic Decision Structures for Hierarchical Systems. *IEEE Transaction on Systems, Man, and Cybernetics*, Vo. 12, No. 3, pp. 325-329

Tiago Facchin and Miguel Afonso Sellitto

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50288
