Skip to main content
SpringerLink
Log in

The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the on-board simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bjerknes J, Winfield A (2013) On fault tolerance and scalability of swarm robotic systems. In: Martinoli A, Mondada F, Correll N, Mermoud G, Egerstedt M, Hsieh MA, Parker LE, Sty K (eds) Distributed autonomous robotic systems, Springer tracts in advanced robotics, vol 83. Springer, Berlin, pp 431–444

    Chapter  Google Scholar 

  2. Bongard J, Zykov V, Lipson H (2006) Resilient machines through continuous self-modeling. Science 314:1118–1121

    Article  Google Scholar 

  3. Eiben AE, Haasdijk E, Bredeche N (2010) Embodied, on-line, on-board evolution for autonomous robotics. In: Levi P, Kernbach S (eds) Symbiotic multi-robot organisms. Springer, Berlin, pp 367–388

  4. Eiben A, Karafotias G, Haasdijk E (2010) Self-adaptive mutation in on- line, on-board evolutionary robotics. In: Proceedings of the Workshop on Self-Organisation in Pervasive Adaptive Systems (PerAda) at the Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2010). IEEE Press, Piscataway

  5. Haasdijk E, Eiben A, Karafotias G (2010) On-line evolution of robot controllers by an encapsulated evolution strategy. In: Proceedings of the 2010 IEEE Congress on Evolutionary Computation, Barcelona, Spain, 2010b. IEEE Computational Intelligence Society, IEEE Press

  6. Hayes A, Martinoli A, Goodman R (2003) Swarm robotic odor localization: off-line optimization and validation with real robots. Robotica 21(4):427–441

    Article  Google Scholar 

  7. Jakobi N, Husbands P, Harvey I (1995) Noise and the reality gap: the use of simulation in evolutionary robotics. In: Advances in Artificial Life: Proceedings of 3rd European Conference on Artificial Life, Springer, pp 704–720

  8. Kernbach S, Meister E, Scholz O, Humza R, Liedke J, Ricotti L, Jemai J, Havlik J, Liu, W (2009) Evolutionary robotics: the next-generation-platform for on-line and on-board artificial evolution. In: Evolutionary Computation, 2009. CEC ’09. IEEE Congress on, pp 1079–1086

  9. Koos S, Mouret JB, Doncieux S (2013) The transferability approach: crossing the reality gap in evolutionary robotics. IEEE Trans Evol Comput 17(1):122–145

    Article  Google Scholar 

  10. Knig L, Jebens K, Kernbach S, Levi P (2008) Stability of on-line and on-board evolving of adaptive collective behavior. In: Bruyninckx H, Preucil L, Kulich M (eds) European robotics symposium 2008, Springer tracts in advanced robotics, vol 44. Springer, Berlin, pp 293–302

    Chapter  Google Scholar 

  11. Liu W, Winfield A (2011) Open-hardware e-puck linux extension board for experimental swarm robotics research. Microprocess Microsyst 35(1):60–67

    Article  Google Scholar 

  12. Liu W, Winfield AF (2009) A macroscopic probabilistic model of adaptive foraging in swarm robotics systems. In: 6th Vienna International Conference on Mathematical Modelling, Vienna, pp 11–13

  13. Mataric M, Cliff D (1996) Challenges in evolving controllers for physical robots. Rob Auton Syst 19(1):67–83

    Article  Google Scholar 

  14. Miglino O, Lund H, Nolfi S (1996) Evolving mobile robots in simulated and real environments. Artif Life 2:417–434

    Article  Google Scholar 

  15. Mondada F, Bonani M, Raemy X, Pugh J, Cianci C, Klaptocz A, Magnenat S, Zufferey J, Floreano D, Martinoli A (2009) The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, pp 59–65

  16. Nolfi S, Parisi D (1995) Evolving non-trivial behaviors on real robots: an autonomous robot that picks up objects. In: Gori M, Soda G (eds) Topics in artificial intelligence, vol 992., Lecture Notes in Computer ScienceSpringer, Berlin, pp 243–254

    Chapter  Google Scholar 

  17. Nolfi S, Parisi D (1997) Learning to adapt to changing environments in evolving neural networks. Adapt Behav 5(1):75–98

  18. O’Dowd P, Winfield A, Studley M (2010) Towards accelerated distributed evolution for adaptive behaviours in swarm robotics. In: Proceedings of Towards Autonomous Robotic Systems (TAROS 2010), pp 169–175

  19. Schlachter F, Schwarzer C, Kernbach S, Michiels N, Levi P (2010) Incremental online evolution and adaptation of neural networks for robot control in dynamic environments. In: Adaptive 2010, the second international conference on adaptive and self-adaptive systems and applications, pp 111–116

  20. Schut M, Haasdijk E, Eiben A (2009) What is situated evolution? In: Evolutionary Computation, 2009. CEC ’09. IEEE Congress on, pp 3277–3284

  21. Seeley TD (2002) When is self-organization used in biological systems? Biol Bull 202(3):314–318

    Article  Google Scholar 

  22. Tomassini M (2005) Spatially structured evolutionary algorithms: artificial evolution in space and time (natural computing series). Springer, Secaucus

    Google Scholar 

  23. Trianni V (2006) On the evolution of self-organising behaviours in a swarm of autonomous robots

  24. Watson RA, Ficici SG, Pollack JB (2002) Embodied evolution: distributing an evolutionary algorithm in a population of robots. Rob Auton Syst 39(1):1–18

    Article  Google Scholar 

  25. Zagal JC, Ruiz-Del-Solar J (2007) Combining simulation and reality in evolutionary robotics. J Intell Rob Syst 50(1):19–39

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of the West of England, Bristol, UK

    Paul J. O’Dowd, Matthew Studley & Alan F. T. Winfield

Authors
  1. Paul J. O’Dowd
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Studley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alan F. T. Winfield
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul J. O’Dowd.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Dowd, P.J., Studley, M. & Winfield, A.F.T. The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours. Evol. Intel. 7, 95–106 (2014). https://doi.org/10.1007/s12065-014-0112-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-014-0112-8

Keywords

Search

Navigation