Skip to main content
Log in

Development of an intelligent agent-based AGV controller for a flexible manufacturing system

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Automated guided vehicles (AGVs) are the most flexible means to transport materials among workstations of a flexible manufacturing system. Complex issues associated with the design of AGV control of these systems are conflict-free shortest path, minimum time motion planning and deadlock avoidance. This research presents an intelligent agent-based framework to overcome the inefficacies associated with the aforementioned issues. Proposed approach describes the operational control of AGVs by integrating different activities such as path generation, journey time enumeration, collision and deadlock identification, waiting node location and its time estimation, and decision making on the selection of the conflict-free shortest feasible path. It represents efficient algorithms and rules associated with each agent for finding the conflict-free minimum time motion planning of AGVs, which are needed to navigate unidirectional and bidirectional flow path network. A collaborative architecture of AGV agent and its different modules are also presented. Three complex experimental scenarios are simulated to test the robustness of the proposed approach. It is shown that the proposed agent-based controller is capable of generating optimal, collision- and deadlock-free path with less computational efforts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Stecke KE, Solberg JJ (1983) Loading and control policies for a flexible manufacturing system. Int J Prod Res 19(5):481–490

    Article  Google Scholar 

  2. Maxwell WL, Muckstatd JA (1982) Design of automated guided vehicle systems. IIE Trans 14:114–124

    Google Scholar 

  3. Jennings NR, Wooldridges M (1995) Applying agent technology. Appl Artif Intell 9:357–369

    Article  Google Scholar 

  4. Tanchoco JMA, Sinriech D (1992) Osl-optimal single-loop guide paths for AGVS. Int J Prod Res 30:665–681

    Article  Google Scholar 

  5. Egbelu PJ, Tanchoco JMA (1986) Potential fore bidirectional guide path for an automated guided vehicles based systems. Int J Prod Res 24:1075–1097

    Article  Google Scholar 

  6. Egbelu PJ (1987) The use of non simulation approaches in estimating vehicle requirements in an automated guided vehicles based transport system. Mater Flow 4:17–32

    Google Scholar 

  7. Viswanadham N, Narahari Y, Johnson TL (1990) Deadlock prevention deadlock avoidance in flexible manufacturing system using Petrinet models. IEEE Trans Robot Autom 6(6):713–723

    Article  Google Scholar 

  8. Kumar RR, Singh AK, Tiwari MK (2004) A fuzzy based algorithm to solve the machine loading problems of an FMS and its neuro fuzzy petri net model. Int J Adv Manuf Technol 23(2–3):318–341

    Article  Google Scholar 

  9. Rajotia S, Shanker K, Batra JL (1998) A semi-dynamic time window constrained routing strategy in an AGV system. Int J Prod Res 36(1):35–50

    Article  MATH  Google Scholar 

  10. Lee CC, Lin JT (1995) Deadlock prediction and avoidance based on Petri nets for zone-control automated guided vehicle systems. Int J Prod Res 33:3249–3265

    Article  MATH  Google Scholar 

  11. Reveliotis SA (2000) Conflict resolution in AGV systems. IIE Trans 32:647–659

    Google Scholar 

  12. Broadbent AJ, Besant CB, Premi SK, Walker SP (1985) Free ranging AGV systems: promises, problems and pathways, problems and pathways. Proceedings of the 2nd International Conference on Automated Material Handling UK, pp 221–237

  13. Tagaboni, F, Tanchoco JMA (1988) A LISP-based controller for free ranging automated guided vehicle systems. Int J Prod Res 26:173–188

    Article  Google Scholar 

  14. Chang WK, Tanchoco JMA (1991) Conflict-free shortest time bidirectional AGV routing. Int J Prod Res 29:2377–2391

    Article  MATH  Google Scholar 

  15. Narshimhan R, Batta R, Karwan M (1998) Routing automated guided vehicles in the presence of interruption. Int J Prod Res 37:653–681

    Article  Google Scholar 

  16. Oboth CR, Batta R, Karwan M (1999) Dynamic conflict-free routing of automated guided vehicle. Int J Prod Res 37:2003–2030

    Article  MATH  Google Scholar 

  17. Choi KH, Kim SC, Yook SH (2000) Multi-agent hybrid shop floor control system. Int J Prod Res 38:4193–4203

    Article  MATH  Google Scholar 

  18. Lu TP, Yih H (2001) An agent based production control framework for multiple line collaborative manufacturing. Int J Prod Res 39:2155–2176

    Article  MATH  Google Scholar 

  19. Wallace A (2001) Application of AI to AGV control agent: agent control of AGVs. Int J Prod Res 39:709–726

    Article  MATH  Google Scholar 

  20. Lim JK, Lim JM, Yoshimoto K, Kim KH, Takahashi T (2002) A construction algorithm for designing guide paths of automated guided vehicle systems. Int J Prod Res 40(15):3981–3994

    Article  MATH  Google Scholar 

  21. Fanti MP (2002) Event-based controller to avoid deadlock and collision in zone control AGVS. Int J Prod Res 40(6):1453–1478

    Article  Google Scholar 

  22. Lee JH, Lee BH, Choi MH (1998) A real time traffic control scheme of multiple AGV systems for collision free minimum time motion: a routing table approach. IEEE Trans Syst Man Cybern, Part A, Syst Humans 28(3):347–358

    Article  Google Scholar 

  23. Kim BI, Graves RJ, Heragu SS, Onge AS (2002) Intelligent agent modeling of an industrial warehousing problem. IIE Trans 34:601–612

    Google Scholar 

  24. Uzam M (2004) The use of Petrinet reduction approachfor an optimal deadlock prevention policy for FMS. Int J Adv Manuf Technol 23(3–4):204–220

    Article  Google Scholar 

  25. Huang J, Palekar US, Kapoor SG (1997) A labeling algorithm for the navigation of automated guided vehicles. Trans ASME J Eng Ind 115:315–321, Aug 1997

    Google Scholar 

  26. Miller RK (1987) Automated guided vehicles and automated manufacturing. Dearborn, MI, Soc Manufact Eng

  27. Fisher M (1994) Representing and executing agent based systems. In: Proceedings of the ECAI’94 Workshop on agent theories. Architecture and languages. Springer, Berlin Heidelberg New York, pp 307–323

  28. Jennings NR, Wooldridge M (1998) Application of intelligent agents. In: Jennings NR, Wooldridge MJ (eds) Agent technology foundation, applications and markets. Springer, Berlin Heidelberg New York, pp 3–28

    Google Scholar 

  29. Davidsson P, Astor E, Ekdah LB (1994) A framework for autonomous agent based on the concept of anticipatory systems. Proceedings of cybernetics and systems’94, vol. II. World Scientific, Singapore, pp 1427–1434

  30. Maes P (1995) Modeling adaptive autonomous agents. In: Langton CG (ed) Artificial life: an overview. MIT Press, Cambridge, MA, pp 135–162

    Google Scholar 

  31. Nwana HS, Ndumu DT (1997) An introduction to agent technology in software agents and soft computing. In: Nwana HS, Azami N (eds) Towards enhancing machine intelligence. Springer, Berlin Heidelberg New York, pp 3–26

    Google Scholar 

  32. Huang C-Y, Nof SY (2000) Formation of autonomous agent networks for manufacturing systems. Int J Prod Res 38(3):607–624

    Article  MATH  Google Scholar 

  33. Wooldridge M, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10:115–152

    Article  Google Scholar 

  34. Jennings NR, Wooldridge M (2000) Agent-oriented software engineering. Handbook of agent technology. AAAI/MIT Press

  35. Lou P, Zhou Z, Chan YP, Xi W (2004) Study on multi-agent based. Agile Supply Chain 23(3–4):197–204

    Google Scholar 

  36. Singh SP, Tiwari MK (2002) Intelligent agent framework to determine the optimal conflict-free path for an automated guided vehicle system. Int J Prod Res 40(16):4195–4223

    Article  MATH  Google Scholar 

  37. Mondal S, Tiwari MK (2002) Application of autonomous agent network to support the architecture of a Holonic manufacturing system. Int J Adv Manuf Technol 20:931–942

    Article  Google Scholar 

  38. Mondal S, Tiwari MK (2003) Formulation of mobile agents for integration of supply chain using the KLAIM concept. Int J Prod Res 41(1):97–119

    Article  Google Scholar 

  39. Pierce AR (1975) Bibliography on algorithms for shortest path, shortest spanning tree, and related circuit routing problems (1956–1974). Networks 5:129–149

    MATH  MathSciNet  Google Scholar 

  40. Dung DA, Grover WD, MacGregor MH (1994) Comparison of k-shortest paths and maximum flow routing for network facility restoration. IEEE J Sel Areas Commun 12(1):88–99

    Article  Google Scholar 

  41. Katoh N, Ibaraki T, Mine H (1982) An efficient algorithm for K shortest simple paths. Networks 12:411–427

    Article  MATH  MathSciNet  Google Scholar 

  42. Topkis DM (1988) A K shortest path algorithm for adaptive routing in communications networks. IEEE Trans Commun 36(7):855–859

    Article  MATH  MathSciNet  Google Scholar 

  43. Finin T, Fritzon R, McKay D, McEntire R (1993), KQML language and protocol for knowledge and information exchange. Technical Report, University of Maryland, Baltimore

  44. Genesereth MR, Fikes RE (1992) Knowledge interchange format. Version 3.0 reference manual. Technical Report Logic-92-1, Computer Science Department, Stanford University, Palo Alto, CA [http://www.cs.umbc.edu/agents/kse/kif]

  45. Bjork B, Wix J (1991) An introduction to STEP. Technical Report, VTT Technical Research Center of Finland and WixMeLelland Ltd, UK

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. K. Tiwari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Srivastava, S.C., Choudhary, A.K., Kumar, S. et al. Development of an intelligent agent-based AGV controller for a flexible manufacturing system. Int J Adv Manuf Technol 36, 780–797 (2008). https://doi.org/10.1007/s00170-006-0892-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0892-9

Keywords

Navigation