Skip to main content

Multi-Robot Dynamic Task Allocation Using Modified Ant Colony System

  • Conference paper
Book cover Artificial Intelligence and Computational Intelligence (AICI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5855))

Abstract

This paper presents a dynamic task allocation algorithm for multiple robots to visit multiple targets. This algorithm is specifically designed for the environment where robots have dissimilar starting and ending locations. And the constraint of balancing the number of targets visited by each robot is considered. More importantly, this paper takes into account the dynamicity of multi-robot system and the obstacles in the environment. This problem is modeled as a constrained MTSP which can not be transformed to TSP and can not be solved by classical Ant Colony System (ACS). The Modified Ant Colony System (MACS) is presented to solve this problem and the unvisited targets are allocated to appropriate robots dynamically. The simulation results show that the output of the proposed algorithm can satisfy the constraints and dynamicity for the problem of multi-robot task allocation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Stack, J.R., Smith, C.M., Hyland, J.C.: Efficient reacquisition path planning for multiple autonomous underwater robots. In: Ocean 2004 - MTS/IEEE Techno-Ocean 2004 Bridges across the Oceans, pp. 1564–1569 (2004)

    Google Scholar 

  2. Zhong, Y., Gu, G.C., Zhang, R.B.: New way of path planning for underwater robot group. Journal of Harbin Engineering University 24(2), 166–169 (2003)

    Google Scholar 

  3. Xu, Z.Z., Li, Y.P., Feng, X.S.: Constrained Multi-objective Task Assignment for UUVs using Multiple Ant Colonies System. In: The 2008 ISECS International Colloquium on Computing, Communication, Control, and Management, pp. 462–466 (2008)

    Google Scholar 

  4. Fogel, D.B.: A parallel processing approach to a multiple traveling salesman problem using evolutionary programming. In: Proceedings of the fourth annual symposium on parallel processing, pp. 318–326 (1990)

    Google Scholar 

  5. Song, C., Lee, K., Lee, W.D.: Extended simulated annealing for augmented TSP and multi-salesmen TSP. In: Proceedings of the international joint conference on neural networks, vol. 3, pp. 2340–2343 (2003)

    Google Scholar 

  6. Ryan, J.L., Bailey, T.G., Moore, J.T., Carlton, W.B.: Reactive Tabu search in unmanned aerial reconnaissance simulations. In: Proceedings of the 1998 winter simulation conference, vol. 1, pp. 873–879 (1998)

    Google Scholar 

  7. Tang, L., Liu, J., Rong, A., Yang, Z.: A multiple traveling salesman problem model for hot rolling scheduling in Shangai Baoshan Iron & Steel Complex. European Journal of Operational Research 124, 267–282 (2000)

    Article  MATH  Google Scholar 

  8. Modares, A., Somhom, S., Enkawa, T.: A self-organizing neural network approach for multiple traveling salesman and robot routing problems. International Transactions in Operational Research 6, 591–606 (1999)

    Article  MathSciNet  Google Scholar 

  9. Pan, J.J., Wang, D.W.: An ant colony optimization algorithm for multiple traveling salesman problem. In: Proceedings of the first international conference on innovative computing, information and control (2006)

    Google Scholar 

  10. Kara, I., Bektas, T.: Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 1449–1458 (2006)

    Google Scholar 

  11. Xu, Z.Z., Li, Y.P., Feng, X.S.: A Hierarchical control system for heterogeneous multiple uuv cooperation task. Robot 30(2), 155–159 (2008)

    Google Scholar 

  12. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, Z., Xia, F., Zhang, X. (2009). Multi-Robot Dynamic Task Allocation Using Modified Ant Colony System. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05253-8_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05252-1

  • Online ISBN: 978-3-642-05253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics