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How to Engineer Robotic Organisms and Swarms?

Bio-Inspiration, Bio-Mimicry, and Artificial Evolution in Embodied Self-Organized Systems

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Bio-Inspired Self-Organizing Robotic Systems

Part of the book series: Studies in Computational Intelligence ((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.

This work is supported by the following grants: EU-IST-FET ‘SYMBRION’, no. 216342; EU-ICT ‘REPLICATOR’, no. 216240; EU-IST FET ‘I-SWARM’, no. 507006; FWF (Austrian Science Fund), no. P19478-B16.

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References

  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. 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. Carrol, S.B.: Endless Forms Most Beautiful: The New Science of Evo Devo. W. W. Norton, New York (2006)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  7. Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  8. Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: From architectures to learning. Evolutionary Intelligence 1, 47–62 (2008)

    Article  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  12. Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbor (1975)

    Google Scholar 

  13. Jasmine. Swarm robot - project website (2010), http://www.swarmrobot.org/

  14. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings IEEE International Conference on Neural Networks, vol. 4 (1995)

    Google Scholar 

  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)

    Article  Google Scholar 

  16. Levi, P., Kernbach, S. (eds.): Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  17. Mattiussi, C., Floreano, D.: Analog genetic encoding for the evolution of circuits and networks. IEEE Transactions on evolutionary computation 11, 596–607 (2007)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  19. Meinhardt, H., Gierer, A.: Pattern formation by local self-activation and lateral inhibition. Bioessays 22, 753–760 (2000)

    Article  Google Scholar 

  20. Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2004)

    Google Scholar 

  21. Pfeiffer, R., Bongard, J.C.: How the Body Shapes the Way We Think. MIT Press, Cambridge (2006)

    Google Scholar 

  22. Rechenberg, I.: Evolutionsstrategie 1994. Frommann Holzboog (1994)

    Google Scholar 

  23. REPLICATOR. Project website (2010), http://www.replicators.eu

  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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  35. Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research 21(1), 63–100 (2004)

    Google Scholar 

  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. Svennebring, J., Koenig, S.: Building terrain-covering ant robots: A feasibility study. Autonomous Robots 16(3), 313–332 (2004)

    Article  Google Scholar 

  38. SYMBRION. Project website (2010), http://www.symbrion.eu

  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. 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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Artificial Life Lab of the Department for Zoology, Karl-Franzens University Graz, Universitä”tsplatz 2, 8010, Graz, Austria

    Thomas Schmickl

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  1. Thomas Schmickl
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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Castle Point on Hudson, 07030, Hoboken, NJ, USA

    Yan Meng

  2. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

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Schmickl, T. (2011). How to Engineer Robotic Organisms and Swarms?. In: Meng, Y., Jin, Y. (eds) Bio-Inspired Self-Organizing Robotic Systems. Studies in Computational Intelligence, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20760-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-20760-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20759-4

  • Online ISBN: 978-3-642-20760-0

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