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Frequency-Based Multi-agent Patrolling Model and Its Area Partitioning Solution Method for Balanced Workload

  • Vourchteang Sea
  • Ayumi Sugiyama
  • Toshiharu Sugawara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)

Abstract

Multi-agent patrolling problem has received growing attention from many researchers due to its wide range of potential applications. In realistic environment, e.g., security patrolling, each location has different visitation requirement according to the required security level. Therefore, a patrolling system with non-uniform visiting frequency is preferable. The difference in visiting frequency generally causes imbalanced workload amongst agents leading to inefficiency. This paper, thus, aims at partitioning a given area to balance agents’ workload by considering that different visiting frequency and then generating route inside each sub-area. We formulate the problem of frequency-based multi-agent patrolling and propose its semi-optimal solution method, whose overall process consists of two steps – graph partitioning and sub-graph patrolling. Our work improve traditional k-means clustering algorithm by formulating a new objective function and combine it with simulated annealing – a useful tool for operations research. Experimental results illustrated the effectiveness and reasonable computational efficiency of our approach.

Keywords

Frequency-based patrolling Graph partitioning Balanced workload Multi-agent systems Linear programming k-means based Simulated annealing 

Notes

Acknowledgments

This work is partly supported by JSPS KAKENHI grant number 17KT0044.

References

  1. 1.
    Almeida, A., Ramalho, G., Santana, H., Tedesco, P., Menezes, T., Corruble, V., Chevaleyre, Y.: Recent advances on multi-agent patrolling. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 474–483. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28645-5_48CrossRefGoogle Scholar
  2. 2.
    Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3), 209–219 (2006)CrossRefGoogle Scholar
  3. 3.
    Elor, Y., Bruckstein, A.M.: Multi-a(ge)nt graph patrolling and partitioning. In: 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, pp. 52–57 (2009)Google Scholar
  4. 4.
    Elth, O., Benno, O., Maarten van, S., Frances, B.: A method for decentralized clustering in large multi-agent systems. In: AAMAS 2003, pp. 789–796 (2003)Google Scholar
  5. 5.
    I-Ming, C., Bruce, L.G., Edward, A.W.: The team orienteering problem. Eur. J. Oper. Res. 88(3), 464–474 (1996)CrossRefGoogle Scholar
  6. 6.
    Jeyhun, K., Murat, O.: Clustering quality improvement of k-means using a hybrid evolutionary model. Procedia Comput. Sci. 61, 38–45 (2015)CrossRefGoogle Scholar
  7. 7.
    Mihai-Ioan, P., Hervé, R., Olivier, S.: Multi-robot patrolling in wireless sensor networks using bounded cycle coverage. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 169–176 (2016)Google Scholar
  8. 8.
    Nallusamy, R., Duraiswamy, K., Dhanalaksmi, R., Parthiban, P.: Optimization of non-linear mutiple traveling salesman problem using k-means clustering, shrink wrap algorithm and meta-heuristics. Int. J. Non Linear Sci. 9(2), 171–177 (2010)zbMATHGoogle Scholar
  9. 9.
    Fazli, P., Alireza, D., Alan, K.M.: Multi-robot repeated area coverage. Auton. Robot 34, 251–276 (2013)CrossRefGoogle Scholar
  10. 10.
    Portugal, D., Rocha, R.: MSP algorithm: muti-robot patrolling based on territory allocation using balanced graph partitioning. In: SAC 2010, pp. 1271–1276 (2010)Google Scholar
  11. 11.
    Portugal, D., Pippin, C., Rocha, R.P., Christensen, H.: Finding optimal routes for multi-robot patrolling in generic graphs. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 363–369 (2014)Google Scholar
  12. 12.
    Portugal, D., Rocha, R.: A survey on multi-robot patrolling algorithms. In: Camarinha-Matos, L.M. (ed.) DoCEIS 2011. IAICT, vol. 349, pp. 139–146. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19170-1_15CrossRefGoogle Scholar
  13. 13.
    Sea, V., Sugawara, T.: Area partitioning method with learning of dirty areas and obstacles in environments for cooperative sweeping robots. In: 2015 IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 523–529 (2015)Google Scholar
  14. 14.
    Sea, V., Kato, C., Sugawara, T.: Coordinated area partitioning method by autonomous agents for continuous cooperative tasks. J. Inf. Process. (JIP) 25, 75–87 (2017)Google Scholar
  15. 15.
    Sugiyama, A., Sea, V., Sugawara, T.: Effective task allocation by enhancing divisional cooperation in multi-agent continuous patrolling tasks. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 33–40 (2016)Google Scholar
  16. 16.
    Tao, M., Laura, E.R.: Frequency-based patrolling with heterogeneous agents and limited communication. arXiv preprint arXiv: 1402.1757 (2014)
  17. 17.
    Sak, T., Wainer, J., Goldenstein, S.K.: Probabilistic multiagent patrolling. In: Zaverucha, G., da Costa, A.L. (eds.) SBIA 2008. LNCS (LNAI), vol. 5249, pp. 124–133. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88190-2_18CrossRefGoogle Scholar
  18. 18.
    Yann, C., Francois, S., Geber, R.: A theoretical analysis of multi-agent patrolling strategies. In: Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1524–1525 (2004)Google Scholar
  19. 19.
    Yann, C.: Theoretical analysis of the multi-agent patrolling problem. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), pp. 302–308 (2004)Google Scholar
  20. 20.
    Yasuyuki, S., Hirofumi, O., Tadashi, M., Maya, H.: Cooperative capture by multi-agent using reinforcement learning application for security patrol systems. In: 2015 10th Asian Control Conference (ASCC), pp. 1–6 (2015)Google Scholar
  21. 21.
    Yehuda, E., Noa, A., Gal, A.K.: Multi-robot area patrol under frequency constraints. In: 2007 IEEE International Conference on Robotics and Automation, pp. 385–390 (2007)Google Scholar
  22. 22.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Vourchteang Sea
    • 1
  • Ayumi Sugiyama
    • 1
  • Toshiharu Sugawara
    • 1
  1. 1.Department of Computer Science and Communications EngineeringWaseda UniversityTokyoJapan

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