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Cooperative Multi-agent Systems for the Multi-target \(\upkappa \)-Coverage Problem

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Agents and Artificial Intelligence (ICAART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12613))

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Abstract

When multiple robots are required to collaborate in order to accomplish a specific task, they need to be coordinated in order to operate efficiently. To allow for scalability and robustness, we propose a novel distributed approach performed by autonomous robots based on their willingness to interact with each other. This willingness, based on their individual state, is used to inform a decision process of whether or not to interact with other robots within the environment. We study this new mechanism to form coalitions in the on-line multi-object \(\upkappa \)-coverage problem, and evaluate its performance through two sets of experiments, in which we also compare to other methods from the state-of-art. In the first set we focus on scenarios with static and mobile targets, as well as with a different number of targets. Whereas in the second, we carry out an extensive analysis of the best performing methods focusing only on mobile targets, while also considering targets that appear and disappear during the course of the experiments. Results show that the proposed method is able to provide comparable performance to the best methods under study.

This work was supported by the DPAC research profile funded by KKS (20150022), the FIESTA project funded by KKS, and the UNICORN project funded by VINNOVA.

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Notes

  1. 1.

    The code for running the simulations is publicly available at https://github.com/gitting-around/kcoverage_ICAART21.git.

  2. 2.

    The present paper is an extension of previous work [12], which contains the account on the experiments and results for Set I.

  3. 3.

    In the final time-step the multi-agent system shuts down, hence this time-step is not considered when dealing with the results.

References

  1. Bakhshipour, M., Jabbari Ghadi, M., Namdari, F.: Swarm robotics search & rescue: a novel artificial intelligence-inspired optimization approach. Appl. Soft Comput. 57, 708–726 (2017)

    Article  Google Scholar 

  2. Burgard, W., Moors, M., Schneider, F.: Collaborative exploration of unknown environments with teams of mobile robots. In: Beetz, M., Hertzberg, J., Ghallab, M., Pollack, M.E. (eds.) Advances in Plan-Based Control of Robotic Agents. LNCS (LNAI), vol. 2466, pp. 52–70. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-37724-7_4

    Chapter  MATH  Google Scholar 

  3. Calvaresi, D., Dubovitskaya, A., Calbimonte, J.P., Taveter, K., Schumacher, M.: Multi-agent systems and blockchain: results from a systematic literature review. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds.) PAAMS 2018. LNCS (LNAI), vol. 10978, pp. 110–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94580-4_9

    Chapter  Google Scholar 

  4. Casadei, R., Pianini, D., Viroli, M., Natali, A.: Self-organising coordination regions: a pattern for edge computing. In: Riis Nielson, H., Tuosto, E. (eds.) COORDINATION 2019. LNCS, vol. 11533, pp. 182–199. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22397-7_11

    Chapter  Google Scholar 

  5. Castelló Ferrer, E.: The blockchain: a new framework for robotic swarm systems. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) FTC 2018. AISC, vol. 881, pp. 1037–1058. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02683-7_77

    Chapter  Google Scholar 

  6. Eickstedt, D.P., Benjamin, M.R.: Cooperative target tracking in a distributed autonomous sensor network. In: OCEANS 2006, pp. 1–6 (2006)

    Google Scholar 

  7. Elhoseny, M., Tharwat, A., Yuan, X., Hassanien, A.E.: Optimizing K-coverage of mobile WSNs. Expert Syst. Appl. 92, 142–153 (2018)

    Article  Google Scholar 

  8. Esterle, L.: Goal-aware team affiliation in collectives of autonomous robots. In: International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp. 90–99 (2018)

    Google Scholar 

  9. Esterle, L., Lewis, P.R.: Online multi-object k-coverage with mobile smart cameras. In: International Conference on Distributed Smart Cameras, pp. 1–6. ACM (2017)

    Google Scholar 

  10. Esterle, L., Lewis, P.R.: Distributed autonomy and trade-offs in online multiobject k-coverage. Comput. Intell. 36(2), 720–742 (2019)

    Article  Google Scholar 

  11. Frasheri, M., Cürüklü, B., Ekström, M., Papadopoulos, A.V.: Adaptive autonomy in a search and rescue scenario. In: International Conference on Self-Adaptive and Self-Organizing Systems, pp. 150–155 (2018)

    Google Scholar 

  12. Frasheri, M., Esterle, L., Papadopoulos, A.V.: Modeling the willingness to interact in cooperative multi-robot systems. In: 12th International Conference on Agents and Artificial Intelligence (ICAART), vol. 1, pp. 62–72 (2020)

    Google Scholar 

  13. Fusco, G., Gupta, H.: Selection and orientation of directional sensors for coverage maximization. In: Proceedings of the International Conference on Sensor, Mesh and Ad Hoc Communications and Networks, pp. 1–9 (2009)

    Google Scholar 

  14. García, S., Menghi, C., Pelliccione, P., Berger, T., Wohlrab, R.: An architecture for decentralized, collaborative, and autonomous robots. In: International Conference on Software Architecture, pp. 75–7509 (2018)

    Google Scholar 

  15. Guarnieri, M., Debenest, R., Inoh, T., Fukushima, E., Hirose, S.: Development of Helios VII: an arm-equipped tracked vehicle for search and rescue operations. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 39–45 (2004)

    Google Scholar 

  16. Hefeeda, M., Bagheri, M.: Randomized k-coverage algorithms for dense sensor networks. In: International Conference on Computer Communications, pp. 2376–2380 (2007)

    Google Scholar 

  17. Hellmund, A., Wirges, S., Ş. Taş, O., Bandera, C., Salscheider, N.O.: Robot operating system: a modular software framework for automated driving. In: International Conference on Intelligent Transportation Systems, pp. 1564–1570 (2016)

    Google Scholar 

  18. Huang, C.F., Tseng, Y.C.: The coverage problem in a wireless sensor network. Mob. Netw. Appl. 10(4), 519–528 (2005). https://doi.org/10.1007/s11036-005-1564-y

    Article  Google Scholar 

  19. Jung, B., Sukhatme, G.S.: Cooperative multi-robot target tracking. In: Gini, M., Voyles, R. (eds.) Distributed Autonomous Robotic Systems, vol. 7, pp. 81–90. Springer, Tokyo (2006). https://doi.org/10.1007/4-431-35881-1_9

  20. Khan, A., Rinner, B., Cavallaro, A.: Cooperative robots to observe moving targets: review. IEEE Trans. Cybern. 48(1), 187–198 (2018)

    Article  Google Scholar 

  21. King, D.W., Esterle, L., Peterson, G.L.: Entropy-based team self-organization with signal suppression. In: Artificial Life Conference Proceedings, vol. 31, pp. 145–152 (2019)

    Google Scholar 

  22. Kolling, A., Carpin, S.: Cooperative observation of multiple moving targets: an algorithm and its formalization. Int. J. Robot. Res. 26(9), 935–953 (2007)

    Article  Google Scholar 

  23. Kumar, S., Lai, T.H., Balogh, J.: On k-coverage in a mostly sleeping sensor network. In: International Conference on Mobile Computing and Networking, pp. 144–158 (2004)

    Google Scholar 

  24. Lee, S.-H., Choi, H.: The fast bully algorithm: for electing a coordinator process in distributed systems. In: Chong, I. (ed.) ICOIN 2002. LNCS, vol. 2344, pp. 609–622. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45801-8_58

    Chapter  Google Scholar 

  25. Li, J.S., Kao, H.C.: Distributed k-coverage self-location estimation scheme based on Voronoi diagram. IET Commun. 4(2), 167–177 (2010)

    Article  MathSciNet  Google Scholar 

  26. Liu, B., Dousse, O., Nain, P., Towsley, D.: Dynamic coverage of mobile sensor networks. Trans. Parall. Distrib. Syst. 24(2), 301–311 (2013)

    Article  Google Scholar 

  27. Micheloni, C., Rinner, B., Foresti, G.L.: Video analysis in pan-tilt-zoom camera networks. Sign. Process. Mag. 27(5), 78–90 (2010)

    Article  Google Scholar 

  28. Navarro-Serment, L.E., Dolan, J.M., Khosla, P.K.: Optimal sensor placement for cooperative distributed vision. International Conference on Robotics and Automation, vol. 1, pp. 939–944 (2004)

    Google Scholar 

  29. Page, J., Armstrong, R., Mukhlish, F.: Simulating search and rescue operations using swarm technology to determine how many searchers are needed to locate missing persons/objects in the shortest time. In: Naweed, A., Bowditch, L., Sprick, C. (eds.) ASC 2019. CCIS, vol. 1067, pp. 106–112. Springer, Singapore (2019). https://doi.org/10.1007/978-981-32-9582-7_8

    Chapter  Google Scholar 

  30. Parker, L.E., Emmons, B.A.: Cooperative multi-robot observation of multiple moving targets. In: International Conference on Robotics and Automation, vol. 3, pp. 2082–2089 (1997)

    Google Scholar 

  31. Piciarelli, C., Esterle, L., Khan, A., Rinner, B., Foresti, G.L.: Dynamic reconfiguration in camera networks: a short survey. IEEE Trans. Circ. Syst. Video Technol. 26(5), 965–977 (2016)

    Google Scholar 

  32. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5 (2009)

    Google Scholar 

  33. Qureshi, F., Terzopoulos, D.: Distributed coalition formation in visual sensor networks: a virtual vision approach. In: Aspnes, J., Scheideler, C., Arora, A., Madden, S. (eds.) DCOSS 2007. LNCS, vol. 4549, pp. 1–20. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73090-3_1

    Chapter  Google Scholar 

  34. Robin, C., Lacroix, S.: Multi-robot target detection and tracking: taxonomy and survey. Auton. Robots 40(4), 729–760 (2015). https://doi.org/10.1007/s10514-015-9491-7

    Article  Google Scholar 

  35. Ruetten, L., Regis, P.A., Feil-Seifer, D., Sengupta, S.: Area-optimized UAV swarm network for search and rescue operations. In: Computing and Communication Workshop and Conference (CCWC), pp. 0613–0618 (2020)

    Google Scholar 

  36. SanMiguel, J.C., Micheloni, C., Shoop, K., Foresti, G.L., Cavallaro, A.: Self-reconfigurable smart camera networks. Computer 47(5), 67–73 (2014)

    Article  Google Scholar 

  37. Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artif. Intell. 101(1), 165–200 (1998)

    Article  MathSciNet  Google Scholar 

  38. Stormont, D.P.: Autonomous rescue robot swarms for first responders. In: IEEE International Conference Computational Intelligence for Homeland Security and Personal Safety, pp. 151–157 (2005)

    Google Scholar 

  39. Theraulaz, G., Bonabeau, E., Deneubourg, J.L.: Response threshold reinforcements and division of labour in insect societies. Roy. Soc. B Biol. Sci. 265(1393), 327–332 (1998)

    Article  Google Scholar 

  40. Werger, B.B., Matarić, M.J.: From insect to internet: Situated control for networked robot teams. Ann. Math. Artif. Intell. 31(1), 173–197 (2001). https://doi.org/10.1023/A:1016650101473

    Article  Google Scholar 

  41. Yanmaz, E., Yahyanejad, S., Rinner, B., Hellwagner, H., Bettstetter, C.: Drone networks: communications, coordination, and sensing. Ad Hoc Netw. 68, 1–15 (2018)

    Article  Google Scholar 

  42. Ye, D., Zhang, M., Sutanto, D.: Self-adaptation-based dynamic coalition formation in a distributed agent network: a mechanism and a brief survey. IEEE Trans. on Parallel Distrib. Syst. 24(5), 1042–1051 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mälardalen University, Västerås, Sweden

    Mirgita Frasheri & Alessandro Vittorio Papadopoulos

  2. DIGIT, Aarhus University, Aarhus, Denmark

    Lukas Esterle

Authors
  1. Mirgita Frasheri
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  2. Lukas Esterle
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  3. Alessandro Vittorio Papadopoulos
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Correspondence to Mirgita Frasheri .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Frasheri, M., Esterle, L., Papadopoulos, A.V. (2021). Cooperative Multi-agent Systems for the Multi-target \(\upkappa \)-Coverage Problem. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-71158-0_5

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