Heterarchical production control in manufacturing systems using the potential fields concept
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This article deals with the potential field concept and its application to dynamic task allocation and dynamic routing controls of flexible manufacturing systems (FMS). This potential field approach requires increasing the interaction capabilities of the different entities, not only resources but also products themselves. In this approach, products request services from resources, sensing the fields emitted by resources and selecting the field that best satisfies the service request. Many already published approaches that are capable of modelling systems based on the interactions between the entities in manufacturing systems are presented. Then, the potential field concept and its application to FMS control are explained in detail. Next, a potential field model and its application are proposed in the real-time heterarchical control of dynamic resource allocation and dynamic product routing. Using a NetLogo simulation, the potential field model supports hard assumptions, such as dynamic transportation times, limited storage capacities and breakdown events. To validate this model, an ongoing real implementation is presented with the AIP-PRIMECA FMS.
KeywordsPotential field Heterarchical control Dynamic allocation Dynamic routing FMS
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- Arkin, R., Bekey, C., & George, A. (1997). Robot Colonies, ISBN: 978-0-7923-9904-9, 160 p., Hardcover.Google Scholar
- Bahroun, Z., et al. (2008). Modélisation multi-agents pour la simulation de politiques de d’approvisionnement au sein de chaines logistiques, 7e International Conference of MOdelisation and SIMulation - MOSIM’08—From 31 March to 2 April—Paris- France.Google Scholar
- Barraquand, J., & Latombe J. (1989). Robot motion planning: A distibuted representation approach. Research Report, STAN-CS-89-1257, Department of Computer Science, Stanford University.Google Scholar
- Berger, T., Sallez, Y., & Trentesaux, D. (2009). Open control of FMS and its application to potential field, CIRP09, 42nd Conference on Manufacturing Systems, Wed. 3 - Fri. 5, June, Grenoble, France.Google Scholar
- Bousbia, S., & Trentesaux, D. (2002). Self-organization in distributed manufacturing control: state-of-the-art and future trends. IEEE International conference on Systems, Man & Cybernetics, (Hammamet, Tunisa) paper #WA1L1.Google Scholar
- Breton, L., Maza, S., & Catstagna, P. (2004). Simulation Multi_agent de systèmes d’AGVs : Comparaison avec une approche prédictive. 5° Francophone Conference in MOdelisation and SIMulation, MOSIM’04—From 1 to 3 September - Nantes (France).Google Scholar
- Brückner, S. (2000). Return from the Ant synthetic ecosystems for manufacturing control, Thesis Humboldt-University of Berlin, June.Google Scholar
- Clarinet System. (2009). Network connectivity for mobile devices, http://www.clarinetsys.com.
- Ferber, J. (1995). Les systèmes multi- agents—vers une intelligence collective, InterEditions, Paris (ISBN 2- 7296- 0572- X).Google Scholar
- Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In IEEE international conference on robotics and automation (pp. 500–505).Google Scholar
- Koestler A. (1967) The Ghost in the Machine. Hutchinson, Stroundsburg, PAGoogle Scholar
- McLurkin, J. (2004). Stupid robot tricks: A behavior-based distributed algorithm library for programming swarms of robots. Master of science in electrical engineering and computer science at the Massachusetts Institute of Technology.Google Scholar
- Montech Technology. (2008). Conveyor systems, http://www.montech.com.
- Moujahed, S. (2007). Approche multi-agents auto-organisée pour la résolution des contraintes spatiales dans les problèmes de positionnement mono et multi-niveaux. Thesis in Franche-Comté University and Belfort-Montbéliard Technology University.Google Scholar
- Okino, N. (1993). Bionic manufacturing system in flexible manufacturing system: Past—present—future. In J. Peklenik (ed) (pp. 73–95) CIRP, Paris.Google Scholar
- Ounnar, F., Pujo, P., Mekaouche, L., & Giambiasi, N. (2007). Integration of a flat holonic form in an HLA environment. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-008-0106-4.
- Parunak, H. V. D., Brueckner, S., & Sauter, J. (2001). ERIM’s approach to fine-grained agents. In Proceedings of the NASA/JPL workshop on radical agent concepts (WRAC’2001), Greenbelt, MD, Sept. 19–21.Google Scholar
- Peeters, P., Van Brussel, H., Valckenaers, P., Wyns, J., Bongaerts, L., Heikkilä, T., & Kollingbaum, M. (1999). Pheromone based emergent shop floor control system for flexible flow shops. In Proceedings of international workshop IWES’99, Kobe, Japan, Dec. 6–7.Google Scholar
- Sallez, Y., Berger, T., & Trentesaux, D. (2009b). Open-control: a new paradigm for integrated product-driven manufacturing control. In Proceedings of 13th IFAC symposium on information control problems in manufacturing (INCOM ‘09), Russia, June 3–5.Google Scholar
- Wago system. (2009), Innovative Connections, http://www.wago.com.
- Weyns D., Bouck N., Holvoet T. (2008) A field-based versus a protocol-based approach for adaptive task assignment. Katholieke Universiteit Leuven, BelgiumGoogle Scholar
- Wilensky, U. (1999). http://ccl.northwestern.edu/netlogo/. Center for connected learning and computer-based modeling, orthwestern University. Evanston, IL.