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International Conference on Swarm Intelligence

ANTS 2018: Swarm Intelligence pp 30–43Cite as

Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

Abstract

Previous research has shown that automatically combining low-level behaviors into a probabilistic finite state machine produces control software that crosses the reality gap satisfactorily. In this paper, we explore the possibility of adopting behavior trees as an architecture for the control software of robot swarms. We introduce Maple: an automatic design method that combines preexisting modules into behavior trees. To highlight the potential of this control architecture, we present robot experiments in which we compare Maple with Chocolate and EvoStick on two missions: foraging and aggregation. Chocolate and EvoStick are two previously published automatic design methods. Chocolate is a modular method that generates probabilistic finite state machines and EvoStick is a traditional evolutionary robotics method. The results of the experiments indicate that behavior trees are a viable and promising architecture to automatically generate control software for robot swarms.

J. Kuckling and A. Ligot contributed equally to the research and should be considered co–first authors. Behavior trees were originally brought to the attention of the authors by DB. The proposed method was conceived by the four authors. It was implemented and tested by JK and AL. The initial draft of the manuscript was written by JK and AL and then revised by DB and MB. The research was directed by MB.

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Notes

  1. 1.

    In biology this behavior is known as negative phototaxis [28].

References

  1. Becroft, D., Bassett, J., Mejía, A., Rich, C., Sidner, C.L.: AIPaint: a sketch-based behavior tree authoring tool. In: Bulitko, V., Riedl, M.O. (eds.) Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE-11. AAAI Press, Stanford (2011)

    Google Scholar 

  2. Beni, G.: From swarm intelligence to swarm robotics. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 1–9. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_1

    Chapter  Google Scholar 

  3. Birattari, M.: On the estimation of the expected performance of a metaheuristic on a class of instances. How many instances, how many runs? Technical report TR/IRIDIA/2004-01, IRIDIA, Université libre de Bruxelles, Belgium (2004)

    Google Scholar 

  4. Bozhinoski, D., Birattari, M.: Designing control software for robot swarms: software engineering for the development of automatic design methods. In: ACM/IEEE 1st International Workshop on Robotics Software Engineering, RoSE, pp. 33–35. ACM, New York (2018). https://doi.org/10.1145/3196558.3196564

  5. Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)

    Article  Google Scholar 

  6. Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(1), 14–23 (1986)

    Article  Google Scholar 

  7. Burridge, R.R., Rizzi, A.A., Koditschek, D.E.: Sequential composition of dynamically dexterous robot behaviors. Int. J. Robot. Res. 18(6), 534–555 (1999)

    Article  Google Scholar 

  8. Champandard, A.J.: Understanding behavior trees (2007). http://aigamedev.com/open/articles/bt-overview/

  9. Colledanchise, M., Ögren, P.: How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees. IEEE Trans. Robot. 33(2), 372–389 (2017)

    Article  Google Scholar 

  10. Colledanchise, M., Ögren, P.: Behavior trees in robotics and AI: an introduction (2018). https://arxiv.org/abs/1709.00084

  11. Dorigo, M., Birattari, M., Brambilla, M.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)

    Article  Google Scholar 

  12. Duarte, M., Gomes, J., Costa, V., Oliveira, S.M., Christensen, A.L.: Hybrid control for a real swarm robotics system in an intruder detection task. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9598, pp. 213–230. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31153-1_15

    Chapter  Google Scholar 

  13. Floreano, D., Husbands, P., Nolfi, S.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 1423–1451. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Francesca, G., Birattari, M.: Automatic design of robot swarms: achievements and challenges. Front. Robot. AI 3(29), 1–9 (2016)

    Google Scholar 

  15. Francesca, G., et al.: AutoMoDe-chocolate: automatic design of control software for robot swarms. Swarm Intell. 9(2/3), 125–152 (2015)

    Article  Google Scholar 

  16. Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., Birattari, M.: AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8(2), 89–112 (2014)

    Article  Google Scholar 

  17. Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., Birattari, M.: Analysing an evolved robotic behaviour using a biological model of collegial decision making. In: Ziemke, T., Balkenius, C., Hallam, J. (eds.) SAB 2012. LNCS (LNAI), vol. 7426, pp. 381–390. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33093-3_38

    Chapter  Google Scholar 

  18. Garattoni, L., Francesca, G., Brutschy, A., Pinciroli, C., Birattari, M.: Software infrastructure for e-puck (and TAM). Technical report TR/IRIDIA/2015-004, IRIDIA, Université libre de Bruxelles, Belgium (2015)

    Google Scholar 

  19. Garattoni, L., Birattari, M.: Swarm robotics. In: Webster, J. (ed.) Wiley Encyclopedia of Electrical and Electronics Engineering. Wiley, Hoboken (2016). https://doi.org/10.1002/047134608X.W8312

  20. Gutiérrez, Á., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F., Magdalena, L.: Open E-puck range & bearing miniaturized board for local communication in swarm robotics. In: Kosuge, K. (ed.) IEEE International Conference on Robotics and Automation, ICRA, pp. 3111–3116. IEEE Press, Piscataway (2009)

    Google Scholar 

  21. Hasselmann, K., Ligot, A., Francesca, G., Birattari, M.: Reference models for AutoMoDe. Technical report TR/IRIDIA/2018-002, IRIDIA, Université libre de Bruxelles, Belgium (2018)

    Google Scholar 

  22. Hu, D., Gong, Y., Hannaford, B., Seibel, E.J.: Semi-autonomous simulated brain tumor ablation with Raven II surgical robot using behavior tree. In: Parker, L., et al. (eds.) IEEE International Conference on Robotics and Automation, ICRA, pp. 3868–3875. IEEE Press, Piscataway (2015)

    Google Scholar 

  23. Isla, D.: Handling complexity in the Halo 2 AI. In: GDC Proceeding (2005)

    Google Scholar 

  24. Jones, S., Studley, M., Hauert, S., Winfield, A.: Evolving behaviour trees for swarm robotics. In: 13th International Symposium on Distributed Autonomous Robotic Systems (DARS) (2016)

    Google Scholar 

  25. Kuckling, J., Ligot, A., Bozhinoski, D., Birattari, M.: Behavior trees as a control architecture in the automatic design of robot swarms: Supplementary material (2018). http://iridia.ulb.ac.be/supp/IridiaSupp2018-004/index.html

  26. López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)

    Article  MathSciNet  Google Scholar 

  27. Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P.: Towards a unified behavior trees framework for robot control. In: Xi, N., et al. (eds.) IEEE International Conference on Robotics and Automation, ICRA, pp. 5420–5427. IEEE Press, Piscataway (2014)

    Google Scholar 

  28. Menzel, R.: Spectral sensitivity and color vision in invertebrates. In: Autrum, H. (ed.) Comparative Physiology and Evolution of Vision in Invertebrates, pp. 503–580. Springer, Heidelberg (1979). https://doi.org/10.1007/978-3-642-66999-6_9

    Chapter  Google Scholar 

  29. Mondada, F., et al.: The e-puck, a robot designed for education in engineering. In: Gonçalves, P., Torres, P., Alves, C. (eds.) Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, pp. 59–65. Instituto Politécnico de Castelo Branco, Portugal (2009)

    Google Scholar 

  30. Nehaniv, C.L., Dautenhahn, K.: Imitation in Animals and Artifacts. MIT Press, Cambridge (2002)

    Google Scholar 

  31. Ögren, P.: Increasing modularity of UAV control systems using computer game behavior trees. In: Thienel, J., et al. (eds.) AIAA Guidance, Navigation, and Control Conference 2012, pp. 358–393. AIAA Meeting Papers (2012)

    Google Scholar 

  32. Perez, D., Nicolau, M., O’Neill, M., Brabazon, A.: Evolving behaviour trees for the mario AI competition using grammatical evolution. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6624, pp. 123–132. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20525-5_13

    Chapter  Google Scholar 

  33. Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)

    Article  Google Scholar 

  34. Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_2

    Chapter  Google Scholar 

  35. Spears, W.M., Spears, D., Hamann, J.C., Heil, R.: Distributed, physics-based control of swarms of vehicles. Auton. Robot. 17, 137–162 (2004)

    Article  Google Scholar 

  36. Stranieri, A., et al.: IRIDIA’s arena tracking system. Technical report TR/IRIDIA/2013-013, IRIDIA, Université libre de Bruxelles, Belgium (2013)

    Google Scholar 

  37. Trianni, V.: Evolutionary Swarm Robotics. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-77612-3

    Book  Google Scholar 

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Acknowledgements

The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 681872). Mauro Birattari acknowledges support from the Belgian Fonds de la Recherche Scientifique – FNRS.

Author information

Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Jonas Kuckling, Antoine Ligot, Darko Bozhinoski & Mauro Birattari

Authors
  1. Jonas Kuckling
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  2. Antoine Ligot
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  3. Darko Bozhinoski
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  4. Mauro Birattari
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Corresponding author

Correspondence to Mauro Birattari .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Kuckling, J., Ligot, A., Bozhinoski, D., Birattari, M. (2018). Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_3

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  • Online ISBN: 978-3-030-00533-7

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