Artificial Life and Robotics

, Volume 24, Issue 1, pp 127–134 | Cite as

Autonomous task allocation by artificial evolution for robotic swarms in complex tasks

  • Yufei Wei
  • Motoaki Hiraga
  • Kazuhiro OhkuraEmail author
  • Zlatan Car
Original Article


Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issues—the bootstrapping problem and deception—especially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach.


Robotic swarm Evolutionary robotics Autonomous task allocation Task partitioning 


  1. 1.
    Brambilla M, Ferrante E, Birattari M, Dorigo M (2013) Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7(1):1–41Google Scholar
  2. 2.
    Şahin E (2004) Swarm robotics: from sources of inspiration to domains of application. International workshop on swarm robotics. Springer, Berlin, Heidelberg, pp 10–20Google Scholar
  3. 3.
    Trianni V, Nolfi S, Dorigo M (2008) Evolution, self-organization and swarm robotics. In: Blum C, Merkle D (eds) Swarm intelligence. Springer, Berlin, pp 1–41Google Scholar
  4. 4.
    Liu W, Winfield A (2010) Modelling and optimisation of adaptive foraging in swarm robotic systems. Int J Robot Res 29(14):1743–1760Google Scholar
  5. 5.
    Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, CambridgeGoogle Scholar
  6. 6.
    Francesca G, Birattari M (2016) Automatic design of robot swarms: achievements and challenges. Front Robot AI 3:29Google Scholar
  7. 7.
    Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47–62Google Scholar
  8. 8.
    Soysal O, Şahin E (2005) Probabilistic aggregation strategies in swarm robotic systems. In: Proceedings of the 2005 IEEE swarm intelligence symposium, pp 325–332Google Scholar
  9. 9.
    Nouyan S, Campo A, Dorigo M (2008) Path formation in a robot swarm. Swarm Intell 2(1):1–23Google Scholar
  10. 10.
    Groß R, Dorigo M (2009) Towards group transport by swarms of robots. Int J Bio-Inspired Comput 1(1–2):1–13Google Scholar
  11. 11.
    Pini G, Brutschy A, Frison M, Roli A, Dorigo M, Birattari M (2011) Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intell 5(3–4):283–304Google Scholar
  12. 12.
    Gomez F, Miikkulainen R (1997) Incremental evolution of complex general behavior. Adapt Behav 5(3–4):317–342Google Scholar
  13. 13.
    Whitley LD (1991) Fundamental principles of deception in genetic search. Found Genet Algorithms 1:221–241MathSciNetGoogle Scholar
  14. 14.
    Agassounon W, Martinoli A, Goodman R (2001) A scalable, distributed algorithm for allocating workers in embedded systems. IEEE Int Conf Syst Man Cybern 5:3367–3373Google Scholar
  15. 15.
    Parker LE (1998) ALLIANCE: an architecture for fault tolerant multirobot cooperation. IEEE Trans Robot Autom 14(2):220–240MathSciNetGoogle Scholar
  16. 16.
    Krieger MJ, Billeter JB (2000) The call of duty: self-organised task allocation in a population of up to twelve mobile robots. Robot Auton Syst 30(1–2):65–84Google Scholar
  17. 17.
    Agassounon W, Martinoli A (2002) Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 3. ACM, Bologna, pp 1090–1097Google Scholar
  18. 18.
    Brutschy A, Pini G, Pinciroli C, Birattari M, Dorigo M (2014) Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton Agents Multi-agent Syst 28(1):101–125Google Scholar
  19. 19.
    Silva F, Duarte M, Correia L, Oliveriram SM, Christensen AL (2016) Open issues in evolutionary robotics. Evol Comput 24(2):205–236Google Scholar
  20. 20.
    Lehman J, Stanley KO (2011) Abandoning objectives: evolution through the search for novelty alone. Evol Comput 19(2):189–223Google Scholar
  21. 21.
    Lehman J, Stanley KO, Miikkulainen R (2013) Effective diversity maintenance in deceptive domains. In: Proceedings of the 15th annual conference on genetic and evolutionary computation. ACM, pp 215–222Google Scholar
  22. 22.
    Lehman J, Miikkulainen R (2014) Overcoming deception in evolution of cognitive behaviors. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation (GECCO '14). ACM, pp 185–192Google Scholar
  23. 23.
    Christensen AL, Dorigo M (2006) Incremental evolution of robot controllers for a highly integrated task. In: International conference on simulation of adaptive behavior, pp 473–484Google Scholar
  24. 24.
    Togelius J (2004) Evolution of a subsumption architecture neurocontroller. J Intell Fuzzy Syst 15(1):15–20Google Scholar
  25. 25.
    Duarte M, Oliveira SM, Christensen AL (2015) Evolution of hybrid robotic controllers for complex tasks. J Intell Robot Syst 78(3–4):463–484Google Scholar
  26. 26.
    Celis S, Hornby G.S, Bongard J (2013) Avoiding local optima with user demonstrations and low-level control. In: Proceedings of the IEEE congress on evolutionary computation, pp 3403–3410Google Scholar
  27. 27.
    Von HE (1990) Task partitioning: an innovation process variable. Res Policy 19(5):407–418Google Scholar
  28. 28.
    Pini G, Brutschy A, Pinciroli C, Dorigo M, Birattari M (2013) Autonomous task partitioning in robot foraging: an approach based on cost estimation. Adapt Behav 21(2):118–136Google Scholar
  29. 29.
    Beyer HG, Schwefel HP (2002) Evolution strategies: a comprehensive introduction. Nat Comput 1(1):3–52MathSciNetzbMATHGoogle Scholar

Copyright information

© ISAROB 2018

Authors and Affiliations

  • Yufei Wei
    • 1
  • Motoaki Hiraga
    • 1
  • Kazuhiro Ohkura
    • 1
    Email author
  • Zlatan Car
    • 2
  1. 1.Graduate School of EngineeringHiroshima UniversityHigashi-HiroshimaJapan
  2. 2.Faculty of EngineeringUniversity of RijekaRijekaCroatia

Personalised recommendations