Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Optimized Direction Assignment in Roadmaps for Multi-AGV Systems Based on Transportation Flows

  • Conference paper
  • First Online:
Distributed Autonomous Robotic Systems (DARS 2021)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 22))

Included in the following conference series:

  • 724 Accesses

Abstract

In this paper we propose a method for optimizing the design of a roadmap, used for motion coordination of groups of automated guided vehicles for industrial environments. Considering the desired flows among different locations in the environment, we model the problem as a multi-commodity concurrent flow problem, which allows us to assign the directions of the paths in an optimized manner. The proposed solution is validated by means of simulations, exploiting realistic layouts, and comparing the performance of the system with those achieved with a baseline roadmap.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It is worth noting that, given a generic roadmap, multiple paths exist that connect each pair of nodes.

  2. 2.

    It is worth noting that the most commonly traveled paths by the vehicles are generally the shortest paths. Alternative paths are chosen only in the presence of heavy traffic conditions or other unexpected events that would generate large waiting times. As an example, the reader is referred to [10].

  3. 3.

    Differences in the maximum number of AGVs used in the simulations are due to the different size of the scenarios.

References

  1. Abdullah, N., Hua, T.K.: Using ford-fulkerson algorithm and max flow-min cut theorem to minimize traffic congestion in Kota Kinabalu, Sabah. J. Inf. 2(4), 18–34 (2017)

    Google Scholar 

  2. Babonneau, F.: Solving the multicommodity flow problem with the analytic center cutting plane method. Ph.D. thesis, University of Geneva (2006)

    Google Scholar 

  3. Bauguion, P.O., Ben-Ameur, W., Gourdin, E.: Efficient algorithms for the maximum concurrent flow problem. Networks 65(1), 56–67 (2015)

    Article  MathSciNet  Google Scholar 

  4. Beinschob, P., Meyer, M., Reinke, C., Digani, V., Secchi, C., Sabattini, L.: Semi-automated map creation for fast deployment of AGV fleets in modern logistics. Robot. Auton. Syst. 87, 281–295 (2017)

    Article  Google Scholar 

  5. Bullo, F., Cortés, J., Martínez, S.: Distributed Control of Robotic Networks. Applied Mathematics Series. Princeton University Press (2009). http://coordinationbook.info

  6. Chien, T.Q., Lau, N.D.: Algorithm finding maximum concurrent multicommodity linear flow with limited cost in extended traffic network with single regulating coefficient on two-side lines. Int. J. Comput. Netw. Commun. (IJCNC) 9(2) (2017)

    Google Scholar 

  7. Chopin, N., Schafer, C.: Adaptive Monte Carlo on multivariate binary sampling spaces. Working Papers, Centre de Recherche en Economie et Statistique (2010)

    Google Scholar 

  8. Devaurs, D., Siméon, T., Cortés, J.: Optimal path planning in complex cost spaces with sampling-based algorithms. IEEE Trans. Autom. Sci. Eng. 13(2), 415–424 (2015)

    Article  Google Scholar 

  9. Digani, V., Sabattini, L., Secchi, C., Fantuzzi, C.: An automatic approach for the generation of the roadmap for multi-AGV systems in an industrial environment. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, September 2014

    Google Scholar 

  10. Digani, V., Sabattini, L., Secchi, C., Fantuzzi, C.: Ensemble coordination approach in multi-AGV systems applied to industrial warehouses. IEEE Trans. Autom. Sci. Eng. 12(3), 922–934 (2015)

    Article  Google Scholar 

  11. Even, S., Itai, A., Shamir, A.: On the complexity of time table and multi-commodity flow problems. In: 16th Annual Symposium on Foundations of Computer Science (SFCS 1975), pp. 184–193. IEEE (1975)

    Google Scholar 

  12. Garg, N., Koenemann, J.: Faster and simpler algorithms for multicommodity flow and other fractional packing problems. SIAM J. Comput. 37(2), 630–652 (2007)

    Article  MathSciNet  Google Scholar 

  13. Geraerts, R., Overmars, M.H.: A comparative study of probabilistic roadmap planners. In: Boissonnat, J.-D., Burdick, J., Goldberg, K., Hutchinson, S. (eds.) Algorithmic Foundations of Robotics V. STAR, vol. 7, pp. 43–57. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-45058-0_4

    Chapter  Google Scholar 

  14. Kaanodiya, K., Rizwanullah, M.: Minimize traffic congestion: an application of maximum flow in dynamic networks. J. Appl. Math. Stat. Inf. (JAMSI) 8(1), 63–74 (2012)

    Google Scholar 

  15. Moore, E.J., Kichainukon, W., Phalavonk, U.: Maximum flow in road networks with speed-dependent capacities-application to bangkok traffic. Songklanakarin J. Sci. Technol. 35(4) (2013)

    Google Scholar 

  16. Sabattini, L., et al.: The pan-robots project: advanced automated guided vehicle systems for industrial logistics. IEEE Robot. Autom. Mag. 25(1), 55–64 (2018). https://doi.org/10.1109/MRA.2017.2700325

    Article  Google Scholar 

  17. Sabattini, L., Digani, V., Secchi, C., Fantuzzi, C.: Hierarchical coordination strategy for multi-AGV systems based on dynamic geodesic environment partitioning. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, October 2016

    Google Scholar 

  18. Sabattini, L., Secchi, C., Chopra, N.: Decentralized estimation and control for preserving the strong connectivity of directed graphs. IEEE Trans. Cybern. 45(10), 2273–2286 (2015)

    Article  Google Scholar 

  19. Sabattini, L., et al.: Technological roadmap to boost the introduction of AGVs in industrial applications, pp. 203–208 (2013)

    Google Scholar 

  20. Tsardoulias, E., Iliakopoulou, A., Kargakos, A., Petrou, L.: A review of global path planning methods for occupancy grid maps regardless of obstacle density. J. Intell. Robot. Syst. 84(1–4), 829–858 (2016)

    Article  Google Scholar 

  21. Vis, I.F.: Survey of research in the design and control of automated guided vehicle systems. Eur. J. Oper. Res. 170(3), 677–709 (2006)

    Article  MathSciNet  Google Scholar 

  22. Yamuna, M., Raza, A., Anand, K., Kumar, S., Kumar, S.: Traffic control system using maximum flow algorithm. Int. J. Curr. Eng. Technol. 6(5) (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. System Logistics S.p.a., Fiorano Modenese, Italy

    Valerio Digani

  2. Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Modena, Italy

    Lorenzo Sabattini

Authors
  1. Valerio Digani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenzo Sabattini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valerio Digani .

Editor information

Editors and Affiliations

  1. Mechanical Engineering and Science, Kyoto University, Kyoto, Japan

    Fumitoshi Matsuno

  2. Mechanical Systems Engineering, Nagoya University, Nagoya, Japan

    Shun-ichi Azuma

  3. Computer Science and Information Technology, Hokkaido University, Sapporo, Hokkaido, Japan

    Masahito Yamamoto

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Digani, V., Sabattini, L. (2022). Optimized Direction Assignment in Roadmaps for Multi-AGV Systems Based on Transportation Flows. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_5

Download citation

Publish with us

Policies and ethics

Search

Navigation