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Reducing the Range of Perception in Multi-agent Patrolling Strategies

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Abstract

Multi-Agent Patrolling Problems consist in moving agents throughout a graph in order to optimize a collective performance metric. Some strategies from the literature tackle this problem by dispatching decentralized autonomous agents that coordinate themselves merely by sensing and writing information in the nodes. In this work, they are called k-range local strategies, were k indicates the range, in number of edges, of the agents’ sensing capabilities. The 1-range strategies (where agents can sense up to its neighbor nodes) are certainly the most common case in the literature. And only few 0-range strategies (where agents can only sense its current node) were found, although this type of strategy has the advantage of requiring simpler hardware, when applied in the design of real robots. In this work, we propose two higher-level procedures to reduce the perception range of 1-range strategies to 0: the Zr Method and the EZr Method. Applying both methods in 1-range strategies found in the literature, we created twenty new 0-range strategies, which were evaluated in a simulation experiment described and analyzed here. We also developed a prototype of a low-cost patrolling robot that is able to run the 0-range strategies proposed in this work.

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References

  1. Almeida, A., Ramalho, G., Santana, H., Tedesco, P., Menezes, T., Corruble, V., Chevaleyre, Y.: Recent advances on multi-agent patrolling. In: Advances in Artificial Intelligence–SBIA 2004, pp. 474–483. Springer, Berlin (2004)

  2. Baglietto, M., Cannata, G., Capezio, F., Sgorbissa, A.: Multi-robot uniform frequency coverage of significant locations in the environment. In: Distributed Autonomous Robotic Systems, vol. 8, pp. 3–14. Springer, Berlin (2009)

  3. Cannata, G., Sgorbissa, A.: A minimalist algorithm for multirobot continuous coverage. IEEE Trans. Robot. 27(2), 297–312 (2011)

    Article  Google Scholar 

  4. Chevaleyre, Y.: Theoretical analysis of the multi-agent patrolling problem. In: Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004.(IAT 2004), pp. 302–308. IEEE (2004)

  5. Chu, H.N., Glad, A., Simonin, O., Sempe, F., Drogoul, A., Charpillet, F.: Swarm approaches for the patrolling problem, information propagation vs. pheromone evaporation. In: 19th IEEE International Conference on Tools with Artificial Intelligence, 2007. ICTAI 2007, vol. 1, pp. 442–449. IEEE, New York (2007)

  6. Elmaliach, Y., Agmon, N., Kaminka, G.A.: Multi-robot area patrol under frequency constraints. Ann. Math. Artif. Intell. 57(3–4), 293–320 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Elmaliach, Y., Shiloni, A., Kaminka, G.A.: A realistic model of frequency-based multi-robot polyline patrolling. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 63–70. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), Richland, USA. http://www.aamas-conference.org/Proceedings/aamas08/proceedings/mainTrackPapers.htm#t2 (2008)

  8. Elor, Y., Bruckstein, A.M.: Multi-a (ge) nt graph patrolling and partitioning. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 02, pp. 52–57. IEEE Computer Society, Washington, D.C. (2009)

  9. Elor, Y., Bruckstein, A.M.: Autonomous multi-agent cycle based patro- lling. In: Swarm Intelligence, pp. 119–130. Springer, Berlin (2010)

  10. Glad, A., Buffet, O., Simonin, O., Charpillet, F.: Self-organization of patrolling-ant algorithms. In: SASO’09. Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2009, pp. 61–70. IEEE, Berlin (2009)

  11. Glad, A., Simonin, O., Buffet, O., Charpillet, F.: Theoretical study of ant-based algorithms for multi-agent patrolling. In: 18th European Conference on Artificial Intelligence including Prestigious Applications of Intelligent Systems (PAIS 2008)-ECAI 2008, pp. 626–630. IOS Press, Amsterdam (2008)

  12. Koenig, S., Liu, Y.: Terrain coverage with ant robots: A simulation study. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 600–607. ACM, New York (2001)

  13. Koenig, S., Szymanski, B., Liu, Y.: Efficient and inefficient ant coverage methods. Ann. Math. Artif. Intell. 31(1-4), 41–76 (2001)

    Article  Google Scholar 

  14. Machado, A., Ramalho, G., Zucker, J.D., Drogoul, A.: Multi-agent patrolling: An empirical analysis of alternative architectures. In: Multi-Agent-Based Simulation II, pp. 155–170. Springer, Berlin (2002)

  15. Portugal, D., Rocha, R.P.: Distributed multi-robot patrol: A scalable and fault-tolerant framework. Robot. Auton. Syst. 61(12), 1572–1587 (2013)

    Article  Google Scholar 

  16. Poulet, C., Corruble, V., Seghrouchni, A.E.F.: Working as a team: using social criteria in the timed patrolling problem. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 933–938. IEEE, New York (2012)

  17. Sampaio, P.A., Ramalho, G., Tedesco, P.: The gravitational strategy for the timed patrolling. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 113–120. IEEE, New York (2010)

  18. Sampaio, P.A., da S Sousa, R., Rocha, A.N.: New patrolling strategies with short-range perception. In: Proceedings of 13th IEEE Latin American Robotics Symposium and 4th Brazilian Robotics Symposium. IEEE Computer Society, New York (2016)

  19. Svennebring, J., Koenig, S.: Building terrain-covering ant robots: A feasibility study. Auton. Robot. 16 (3), 313–332 (2004)

    Article  Google Scholar 

  20. Wagner, I.A., Lindenbaum, M., Bruckstein, A.M.: Efficiently searching a graph by a smell-oriented vertex process. Ann. Math. Artif. Intell. 24(1-4), 211–223 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  21. Yan, C., Zhang, T.: Multi-robot patrol a distributed algorithm based on expected idleness. Int. J.of Adv. Robot. Syst. 13(6), 1729881416663,666 (2016)

    Google Scholar 

  22. Yanovski, V., Wagner, I.A., Bruckstein, A.M.: A distributed ant algorithm for ∖ protect efficiently patrolling a network. Algorithmica 37(3), 165–186 (2003)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Departamento de Estatística e Informática (DEINFO), Universidade Federal de Pernambuco (UFRPE), CEP: 52171-900, Recife, PE, Brasil

    Pablo Azevedo Sampaio, Rodrigo da Silva Sousa & Alessandro Nazário Rocha

Authors
  1. Pablo Azevedo Sampaio
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  2. Rodrigo da Silva Sousa
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  3. Alessandro Nazário Rocha
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Corresponding author

Correspondence to Pablo Azevedo Sampaio.

Additional information

Research sponsored by FACEPE, project APQ-0882-1.03/4.

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Azevedo Sampaio, P., da Silva Sousa, R. & Nazário Rocha, A. Reducing the Range of Perception in Multi-agent Patrolling Strategies. J Intell Robot Syst 91, 219–231 (2018). https://doi.org/10.1007/s10846-017-0697-0

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  • DOI: https://doi.org/10.1007/s10846-017-0697-0

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