Skip to main content
Book cover

International Conference on Distributed Artificial Intelligence

DAI 2020: Distributed Artificial Intelligence pp 14–28Cite as

LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells

  • 348 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12547)

Abstract

Collision avoidance is one of the most primary problems in the decentralized multiagent navigation: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the local action cell, which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion within the corresponding cell, there should be no collision caused. Furthermore, we coupled the local action cell with an adaptive learning framework, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.

Keywords

  • Buffered Voronoi cell
  • Adaptive learning
  • Multiagent navigation

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-64096-5_2
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-64096-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   74.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    In general, for the parameters that should be specified in the experiments, we tested with several values, including the one recommended in the paper that proposed the considered algorithm, and selected the best choice.

References

  1. Bandyopadhyay, S., Chung, S., Hadaegh, F.Y.: Probabilistic swarm guidance using optimal transport. In: 2014 IEEE Conference on Control Applications (CCA), pp. 498–505, October 2014. https://doi.org/10.1109/CCA.2014.6981395

  2. van den Berg, J.P., Lin, M.C., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008)

    Google Scholar 

  3. Bhattacharya, P., Gavrilova, M.L.: Roadmap-based path planning - using the Voronoi diagram for a clearance-based shortest path. IEEE Robot. Autom. Mag. 15(2), 58–66 (2008). https://doi.org/10.1109/MRA.2008.921540

    CrossRef  Google Scholar 

  4. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998). https://doi.org/10.1177/027836499801700706

    CrossRef  Google Scholar 

  5. Garrido, S., Moreno, L., Blanco, D.: Voronoi diagram and fast marching applied to path planning, vol. 2006, pp. 3049–3054, January 2006. https://doi.org/10.1109/ROBOT.2006.1642165

  6. Godoy, J.E., Karamouzas, I., Guy, S.J., Gini, M.: Adaptive learning for multi-agent navigation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 1577–1585 (2015)

    Google Scholar 

  7. Godoy, J.E., Karamouzas, I., Guy, S.J., Gini, M.L.: Implicit coordination in crowded multi-agent navigation. In: Proceedings of 17th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2487–2493 (2016)

    Google Scholar 

  8. Pimenta, L.C.A., Kumar, V., Mesquita, R.C., Pereira, G.A.S.: Sensing and coverage for a network of heterogeneous robots. In: 2008 47th IEEE Conference on Decision and Control, pp. 3947–3952, December 2008. https://doi.org/10.1109/CDC.2008.4739194

  9. Şenbaşlar, B., Hönig, W., Ayanian, N.: Robust trajectory execution for multi-robot teams using distributed real-time replanning. In: Correll, N., Schwager, M., Otte, M. (eds.) Distributed Autonomous Robotic Systems. SPAR, vol. 9, pp. 167–181. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05816-6_12

    CrossRef  Google Scholar 

  10. van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. STAR, vol. 70, pp. 3–19. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19457-3_1

  11. Zhou, D., Wang, Z., Bandyopadhyay, S., Schwager, M.: Fast, on-line collision avoidance for dynamic vehicles using buffered Voronoi cells. IEEE Robot. Autom. Lett. 2(2), 1047–1054 (2017). https://doi.org/10.1109/LRA.2017.2656241

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, China

    Li Ning & Yong Zhang

Authors
  1. Li Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Ning .

Editor information

Editors and Affiliations

  1. University of Alberta, Edmonton, AB, Canada

    Matthew E. Taylor

  2. Nanjing University, Nanjing, China

    Assoc. Prof. Yang Yu

  3. University of Oxford, Oxford, UK

    Prof. Edith Elkind

  4. Nanjing University, Nanjing, China

    Yang Gao

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ning, L., Zhang, Y. (2020). LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells. In: Taylor, M.E., Yu, Y., Elkind, E., Gao, Y. (eds) Distributed Artificial Intelligence. DAI 2020. Lecture Notes in Computer Science(), vol 12547. Springer, Cham. https://doi.org/10.1007/978-3-030-64096-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64096-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64095-8

  • Online ISBN: 978-3-030-64096-5

  • eBook Packages: Computer ScienceComputer Science (R0)