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Building a Generic Simulation Model for Analyzing the Feasibility of Multi-Robot Task Allocation (MRTA) Problems

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Modelling and Simulation for Autonomous Systems (MESAS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11995))

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Abstract

Multi-Robot Task Allocation (MRTA) will gain much importance by the rise of autonomous vehicles and the Internet of Things (IoT) where several agents coordinate and work for a common goal. Due to their distributed nature, hardware complexity and environmental constraints, constructing and testing multi-robot systems may be expensive, dangerous and time-consuming. MRTA includes sub-problems such as coordination strategy, bid valuation, path planning, terrain complexity, robot design, path optimization, and overall optimization. There is a need for building a generic MRTA model to experiment with these numerous combinations in a controlled and automated fashion. This paper presents the structure of the MRTA generic simulation model which is designed to search for the optimal combination of MRTA taxonomy elements. An MRTA Simulation Tool (MRTASim) is designed to adapt the generic model to specific cases and to run simulations for real-life scenarios. Decision-makers can build their own MRTA models and they can be sure for the feasibility of large distributed and collaborated systems before initiating huge investments.

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References

  1. Ray, P.P.: Internet of robotic things: concept, technologies, and challenges. IEEE Access 4, 9489–9500 (2016)

    Article  Google Scholar 

  2. Rizk, Y., Awad, M., Tunstel, E.W.: Cooperative heterogeneous multi-robot systems: a survey. ACM Comput. Surv. 52(2) (2019). Article no. 29

    Google Scholar 

  3. Ferri, G., Munafo, A., Tesei, A., LePage, K.: A market-based task allocation framework for autonomous underwater surveillance networks. In: OCEANS 2017 - Aberdeen, Aberdeen, pp. 1–10 (2017). https://doi.org/10.1109/oceanse.2017.8084769

  4. Khalil, E.A., Ozdemir, S., Attea, B.A.: A new task allocation protocol for extending stability and operational periods in internet of things. IEEE Internet Things J. 6(4), 7225–7231 (2019)

    Article  Google Scholar 

  5. Öztürk, S., Kuzucuoğlu, A.E.: Multi-robot coordination approach for autonomous runway foreign object debris (FOD) clearance. Robot. Auton. Syst. 75, 244–259 (2016)

    Article  Google Scholar 

  6. Yao, W., Wan, N., Qi, N.: Hierarchical path generation for distributed mission planning of UAVs. In: IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, pp. 1681–1686 (2016)

    Google Scholar 

  7. Das, G.P., McGinnity, T.M., Coleman, S.A., Behera, L.: A distributed task allocation algorithm for a multi-robot system in healthcare facilities. J. Intell. Rob. Syst. 80(1), 33–58 (2014). https://doi.org/10.1007/s10846-014-0154-2

    Article  Google Scholar 

  8. Öztürk, S., Kuzucuoğlu, A.E.: Optimal bid valuation using path finding for multi-robot task allocation. J. Intell. Manuf. 26(5), 1049–1062 (2014). https://doi.org/10.1007/s10845-014-0909-4

    Article  Google Scholar 

  9. Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley, Hoboken (2007). ISBN 978-0-470-05747-6

    Book  Google Scholar 

  10. Gerkey, B.P., Mataric, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. J. Robot. Res. 23(9), 939–954 (2004)

    Article  Google Scholar 

  11. Korsah, G.A., Stentz, A., Dias, M.B.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32(12), 1495–1512 (2013)

    Article  Google Scholar 

  12. Khamis, A., Hussein, A., Elmogy, A.: Multi-robot task allocation: a review of the state-of-the-art. In: Koubâa, A., Martínez-de Dios, J.R. (eds.) Cooperative Robots and Sensor Networks 2015. SCI, vol. 604, pp. 31–51. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18299-5_2

    Chapter  Google Scholar 

  13. Zlot, R., Stentz, A., Dias, M.B., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proceedings of ICRA 2002, Washington, D.C., USA, pp. 3016–3023 (2002)

    Google Scholar 

  14. Oh, G., Kim, Y., Ahn, J., Choi, H.-L.: Market-based task assignment for cooperative timing missions in dynamic environments. J. Intell. Rob. Syst. 87(1), 97–123 (2017). https://doi.org/10.1007/s10846-017-0493-x

    Article  Google Scholar 

  15. Dias, M.B.: TraderBots: a new paradigm for robust and efficient multirobot coordination in dynamic environments. Ph.D. dissertation, Carnegie Mellon University (2004)

    Google Scholar 

  16. Pereyra, E., Araguás, G., Kulich, M.: Path planning for a formation of mobile robots with split and merge. In: Mazal, J. (ed.) MESAS 2017. LNCS, vol. 10756, pp. 59–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76072-8_4

    Chapter  Google Scholar 

  17. Stodola, P.: Route optimization for cooperative aerial reconnaissance. In: Mazal, J. (ed.) MESAS 2017. LNCS, vol. 10756, pp. 83–91. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76072-8_6

    Chapter  Google Scholar 

  18. LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. In: TR 98-11, Computer Science Department, Iowa State University, October (1998)

    Google Scholar 

  19. Koenig, S., Likhachev, M.: D* Lite. In: Proceedings of the AAAI Conference of Artificial Intelligence (AAAI), pp. 476–483 (2002)

    Google Scholar 

  20. Hodicky, J.: HLA as an experimental backbone for autonomous system integration into operational field. In: Hodicky, J. (ed.) MESAS 2014. LNCS, vol. 8906, pp. 121–126. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13823-7_11

    Chapter  Google Scholar 

  21. Hodicky, J.: Standards to support military autonomous system life cycle. In: Březina, T., Jabłoński, R. (eds.) MECHATRONICS 2017. AISC, vol. 644, pp. 671–678. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65960-2_83

    Chapter  Google Scholar 

  22. Gerkey, B., Vaughan, R.T., Howard, A.: The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the International Conference on Advanced Robotics (ICAR 2003), Coimbra, Portugal, 30 June–3 July 2003, pp. 317–323 (2003)

    Google Scholar 

  23. https://cyberbotics.com/#features

  24. https://www.openrobots.org/morse/doc/latest/what_is_morse.html

  25. http://www.coppeliarobotics.com/

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Acknowledgements

The authors would like to thank Scientific and Technological Research Council of Turkey (TUBITAK in Turkish) for funding this study, Clifton G.M. Presser, Rene Grothmann and William Fiset for sharing their TSP Java code, Konstantinos A. Nedas for sharing his Hungarian Algorithm Java code, and Daniel Beard for sharing his DStarLite Java Pathfinding code.

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Authors and Affiliations

  1. Software Testing and Quality Evaluation Laboratory, TUBITAK BILGEM, Gebze, Turkey

    Savaş Öztürk

  2. Electrical and Electronics Engineering Department, Marmara University, İstanbul, Turkey

    Ahmet Emin Kuzucuoğlu

Authors
  1. Savaş Öztürk
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  2. Ahmet Emin Kuzucuoğlu
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Corresponding author

Correspondence to Savaş Öztürk .

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Editors and Affiliations

  1. NATO Modelling and Simulation Centre of Excellence, Rome, Italy

    Jan Mazal

  2. MIRPA Lab, University of Palermo, Palermo, Italy

    Adriano Fagiolini

  3. Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Petr Vasik

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Öztürk, S., Kuzucuoğlu, A.E. (2020). Building a Generic Simulation Model for Analyzing the Feasibility of Multi-Robot Task Allocation (MRTA) Problems. In: Mazal, J., Fagiolini, A., Vasik, P. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2019. Lecture Notes in Computer Science(), vol 11995. Springer, Cham. https://doi.org/10.1007/978-3-030-43890-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-43890-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43889-0

  • Online ISBN: 978-3-030-43890-6

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