Advertisement

How to Engineer Robotic Organisms and Swarms?

Bio-Inspiration, Bio-Mimicry, and Artificial Evolution in Embodied Self-Organized Systems
  • Thomas Schmickl
Part of the Studies in Computational Intelligence book series (SCI, volume 355)

Abstract

In large-scale systems composed of autonomous embodied agents (e.g., robots), unpredictability of events, sensor noise and actuator imperfection pose significant challanges to the designers of control software. If such systems tend to selforganize, emergent phenomena prevent classical engineering approaches per se. In recent years, the Artificial Life Lab at the University of Graz has investigated a variety of methods to synthesize such control algorithms used in multi-modular robotics and in swarm robotics. These methods either translate mechanisms directly from biology to the engineering domain (bio-mimicry, bio-inspiration) or generates such controllers through artificial evolution from scratch. In this article I first discuss distributed control algorithms, which determine the collective behavior of autonomous robotic swarms. These algorithms are derived from collective behavior of honeybees and from slime mold aggregation. One of these algorithms is inspired by inter-adult food exchange in honeybees (’trophallaxis’) another one from chemical signaling in slime molds. In addition to the control of robot swarms, control paradigms for multi-modular robotic organisms are presented, which are again based on simulated fluid exchange (hormones) among compartments of robotic organisms. In both domains -swarms and organisms- the control system is self-organized and consists of many homeostatic sub-systems which adapt to each other on the individual (module) and on the collective level (organism, swarm). Additionally, I discuss the importance of distributed feedback networks, as well as the benefits and drawbacks of bio-inspiration and bio-mimicry in collective robotics.

Keywords

Autonomous Robot Slime Mold Swarm Algorithm Evolutionary Robotic Robotic Swarm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blow, M.: ‘stigmergy’: Biologically-inspired robotic art. In: Proceedings of the Symposium on Robotics, Mechatronics and Animatronics in the Creative and Entertainment Industries and Arts, pp. 1–8 (2005)Google Scholar
  2. 2.
    Bonani, M., Raemy, X., Pugh, J., Mondana, F., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Proc. of the 9th Conference on Autnomous Robot Systems and Competitions, vol. 1, pp. 59–65 (2009)Google Scholar
  3. 3.
    Carrol, S.B.: Endless Forms Most Beautiful: The New Science of Evo Devo. W. W. Norton, New York (2006)Google Scholar
  4. 4.
    Clune, J., Beckmann, B., Ofria, C., Pennock, R.: Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics, Trondheim, Norway (2009)Google Scholar
  5. 5.
    Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC), IEEE, New York (2009)Google Scholar
  6. 6.
    Corradi, P., Schmickl, T., Scholz, O., Menciassi, A., Dario, P.: Optical networking in a swarm of microrobots. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 3(2), 107–119 (2009)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)zbMATHCrossRefGoogle Scholar
  8. 8.
    Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: From architectures to learning. Evolutionary Intelligence 1, 47–62 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Garnier, S., Tache, F., Combe, M., Grimal, A., Theraulaz, G.: Alice in pheromone land: An experimental setup for the study of ant-like robots. In: Swarm Intelligence Symposium, SIS 2007, pp. 37–44. IEEE, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Hamann, H., Stradner, J., Schmickl, T., Crailsheim, K.: A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), pp. 244–251 (2010)Google Scholar
  11. 11.
    Heran, H.: Untersuchungen über den Termperatursinn der Honigbiene (Apis mellifica) unter besonderer Berücksichtigung der Wahrnehmung strahlender Wärme. Zeitschrift für vergleichende Physiologie 34, 179–206 (1952)CrossRefGoogle Scholar
  12. 12.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbor (1975)Google Scholar
  13. 13.
    Jasmine. Swarm robot - project website (2010), http://www.swarmrobot.org/
  14. 14.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings IEEE International Conference on Neural Networks, vol. 4 (1995)Google Scholar
  15. 15.
    Kernbach, S., Thenius, R., Kornienko, O., Schmickl, T.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic swarm. Adaptive Behavior 17, 237–259 (2009)CrossRefGoogle Scholar
  16. 16.
    Levi, P., Kernbach, S. (eds.): Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  17. 17.
    Mattiussi, C., Floreano, D.: Analog genetic encoding for the evolution of circuits and networks. IEEE Transactions on evolutionary computation 11, 596–607 (2007)CrossRefGoogle Scholar
  18. 18.
    Mayet, R., Roberz, J., Schmickl, T., Crailsheim, K.: Antbots: A feasible visual emulation of pheromone trails for swarm robots. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 84–94. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Meinhardt, H., Gierer, A.: Pattern formation by local self-activation and lateral inhibition. Bioessays 22, 753–760 (2000)CrossRefGoogle Scholar
  20. 20.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2004)Google Scholar
  21. 21.
    Pfeiffer, R., Bongard, J.C.: How the Body Shapes the Way We Think. MIT Press, Cambridge (2006)Google Scholar
  22. 22.
    Rechenberg, I.: Evolutionsstrategie 1994. Frommann Holzboog (1994)Google Scholar
  23. 23.
    REPLICATOR. Project website (2010), http://www.replicators.eu
  24. 24.
    Russell, R.A.: Heat trails as short-lived navigational markers for mobile robots. In: Proceedings of International Conference on Robotics and Automation, 1997, vol. 4, pp. 3534–3539 (1997)Google Scholar
  25. 25.
    Russell, R.A.: Ant trails – an example for robots to follow? In: Proceedings of IEEE International Conference on Robotics and Automation, 1999, vol. 4, pp. 2698–2703 (1999)Google Scholar
  26. 26.
    Schmickl, T., Crailsheim, K.: A Navigation Algorithm for Swarm Robotics Inspired by Slime Mold Aggregation. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 1–13. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots 25(1-2), 171–188 (2008)CrossRefGoogle Scholar
  28. 28.
    Schmickl, T., Crailsheim, K.: Modelling a hormone-based robot controller. In: 6th Vienna International Conference on Mathematical Modelling, MATHMOD 2009 (2009)Google Scholar
  29. 29.
    Schmickl, T., Hamann, H., Stradner, J., Crailsheim, K.: Hormone-based control for multi-modular robotics. In: Levi, P., Kernbach, S. (eds.) Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution. Springer, Heidelberg (2010)Google Scholar
  30. 30.
    Schmickl, T., Möslinger, C., Thenius, R., Crailsheim, K.: Individual adaptation allows collective path-finding in a robotic swarm. International Journal of Factory Automation, Robotics and Soft Computing 4, 102–108 (2007)Google Scholar
  31. 31.
    Schmickl, T., Thenius, R., Möslinger, C., Radspieler, G., Kernbach, S., Crailsheim, K.: Get in touch: Cooperative decision making based on robot-to-robot collisions. Autonomous Agents and Multi-Agent Systems 18(1), 133–155 (2008)CrossRefGoogle Scholar
  32. 32.
    Shen, W.-M., Salemi, B., Will, P.: Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots. IEEE Trans. on Robotics and Automation 18(5), 700–712 (2002)CrossRefGoogle Scholar
  33. 33.
    Shen, W.-M., Will, P., Galstyan, A., Chuong, C.-M.: Hormone-inspired self-organization and distributed control of robotic swarms. Autonomous Robots 17, 93–105 (2004)CrossRefGoogle Scholar
  34. 34.
    Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artificial Life 15(2), 185–212 (2009)CrossRefGoogle Scholar
  35. 35.
    Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research 21(1), 63–100 (2004)Google Scholar
  36. 36.
    Stradner, J., Hamann, H., Schmickl, T., Thenius, R., Crailsheim, K.: Evolving a novel bio-inspired controller in reconfigurable robots. In: 10th European Conference on Artificial Life (ECAL 2009). LNCS, Springer, Heidelberg (2010)Google Scholar
  37. 37.
    Svennebring, J., Koenig, S.: Building terrain-covering ant robots: A feasibility study. Autonomous Robots 16(3), 313–332 (2004)CrossRefGoogle Scholar
  38. 38.
    SYMBRION. Project website (2010), http://www.symbrion.eu
  39. 39.
    Thenius, R., Schmickl, T., Crailsheim, K.: Novel concept of modelling embryology for structuring an artificial neural network. In: Troch, I., Breitenecker, F. (eds.) Proceedings of the MATHMOD (2009)Google Scholar
  40. 40.
    Valdastri, P., Corradi, P., Menciassi, A., Schmickl, T., Crailsheim, K., Seyfried, J., Dario, P.: Micromanipulation, communication and swarm intelligence issues in a swarm microrobotic platform. Robotics and Autonomous Systems 54, 789–804 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Schmickl
    • 1
  1. 1.Artificial Life Lab of the Department for ZoologyKarl-Franzens University GrazGrazAustria

Personalised recommendations