Learning Mutation Strategies for Evolution and Adaptation of a Simulated Snakebot

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Wheelless, limbless snake-like robots (Snakebots) feature potential robustness characteristics beyond the capabilities of most wheeled and legged vehicles—ability to traverse terrain that would pose problems for traditional wheeled or legged robots, and insignificant performance degradation when partial damage is inflicted. Some useful features of Snakebots include smaller size of the cross-sectional areas, stability, ability to operate in difficult terrain, good traction, high redundancy, and complete sealing of the internal mechanisms (Dowling in Limbless locomotion: learning to crawl with a snake robot. Doctoral dissertation, Technical Report CMU-RI-TR-97-48. Robotics Institute, Carnegie Mellon University, 1997; Hirose in Biologically inspired robots: snake-like locomotors and manipulators. Oxford University Press, Oxford, 1993). Robots with these properties open up several critical applications in exploration, reconnaissance, medicine and inspection. However, compared to the wheeled and legged vehicles, Snakebots feature (i) more difficult control of locomotion gaits and (ii) inferior speed characteristics. In this work we intend to address the following challenge: how to automatically develop control sequences of Snakebot’s actuators, which allow for achieving the fastest possible speed of locomotion.

Keywords

Pyramid Burial Mellon 

Notes

Acknowledgements

The author thanks Katsunori Shimohara, Thomas Ray and Andrzej Buller for their support of this work.

References

  1. Andrusenko, Y. (2001). Russian culture navigator: Miturich-Khlebnikovs: art trade runs in the family. Available at http://www.vor.ru/culture/cultarch191_eng.html.
  2. Angeline, P. J. (1994). Genetic programming and emergent intelligence. In K. E. Kinnear Jr. (Ed.), Advances in genetic programming (pp. 75–98). Cambridge: MIT Press. Google Scholar
  3. Antonisse, J. (1991). A grammar-based genetic algorithm. In G. J. E. Rawlins (Ed.), Foundations of the genetic algorithm workshop (FOGA) (pp. 193–204). San Francisco: Morgan Kaufmann. Google Scholar
  4. Bongard, J. C., & Lipson, H. (2004). Automated damage diagnosis and recovery for remote robotics. In IEEE proceedings of the 2004 international conference on robotics and automation (ICRA 2004) (pp. 3545–3550). New York: IEEE. CrossRefGoogle Scholar
  5. Bosman, P., & de Jong, E. (2004). Learning probabilistic tree grammars for genetic programming. In Proceedings of the 8th international conference on parallel problem solving from nature PPSN-04 (pp. 192–201). London: Springer. Google Scholar
  6. Bray, T., Paoli, J., Sperberg-McQueen, C. M., & Maler, E. (2000). Extensible Markup Language (XML) 1.0, Second Edition, W3C Recommendation. Available at http://www.w3.org/TR/REC-xml/.
  7. Caporale, L. H. (2003). Darwin in the genome: molecular strategies in biological evolution. New York: McGraw-Hill/Contemporary Books. Google Scholar
  8. Dowling, K. (1997). Limbless locomotion: learning to crawl with a snake robot (Doctoral dissertation, Technical Report CMU-RI-TR-97-48). Robotics Institute, Carnegie Mellon University. Google Scholar
  9. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley. MATHGoogle Scholar
  10. Hirose, S. (1993). Biologically inspired robots: snake-like locomotors and manipulators. Oxford: Oxford University Press. Google Scholar
  11. Ito, K., Kamegawa, K., & Matsuno, F. (2003). Extended QDSEGA for controlling real robots—acquisition of locomotion patterns for snake-like robot. In IEEE proceedings of IEEE international conference on robotics and automation (ICRA 2003) (pp. 791–796). New York: IEEE. CrossRefGoogle Scholar
  12. Kamio, S., Mitsuhashi, H., & Iba, H. (2003). Integration of genetic programming and reinforcement learning for real robots. In E. Cantú-Paz, J. A. Foster, K. Deb, L. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. K. Standish, G. Kendall, S. W. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. A. Dowsland, N. Jonoska, & J. F. Miller (Eds.), Proceedings of the genetic and evolutionary computations conference (GECCO 2003) (pp. 470–482). Berlin: Springer. CrossRefGoogle Scholar
  13. Kimura, H., Yamashita, T., & Kobayashi, S. (2001). Reinforcement learning of walking behavior for a four-legged robot. In Proceedings of 40th IEEE conference on decision and control, Orlando, USA (pp. 411–416). Google Scholar
  14. Kirschner, M. W., & Gerhart, J. C. (2005). The plausibility of life: resolving Darwin’s dilemma. New Haven: Yale University Press. Google Scholar
  15. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press. MATHGoogle Scholar
  16. Levitan, I. B., & Kaczmarek, L. K. (2002). The neuron: cell and molecular biology. New York: Oxford University Press. Google Scholar
  17. Mahdavi, S., & Bentley, P. J. (2003). Evolving motion of robots with muscles. In Proceedings of EvoROB2003, the 2nd European workshop on evolutionary robotic (EuroGP 2003), Essex, UK (pp. 655–664). Google Scholar
  18. O’Neill, M., & Ryan, C. (2003). Grammatical evolution: evolutionary automatic programming in an arbitrary language. Norwell: Kluwer. MATHGoogle Scholar
  19. Pelikan, M., Goldberg, D. E., & Cantú-Paz, E. (1999). BOA: the Bayesian optimization algorithm. In Proceedings of the genetic and evolutionary computation conference (GECCO-99), Orlando, USA (pp. 525–532). Google Scholar
  20. Prokopenko, M., Gerasimov, V., & Tanev, I. (2006). Evolving spatiotemporal coordination in a modular robotic system. In S. Nolfi, G. Baldassarre, R. Calabretta, J. C. T. Hallam, D. Marocco, J.-A. Meyer, O. Miglino, & D. Parisi (Eds.), Lecture notes in computer science: Vol. 4095. From animals to animats 9: 9th international conference on the simulation of adaptive behavior (SAB 2006), Rome, Italy, 25–29 September 2006 (pp. 558–569). Berlin: Springer. CrossRefGoogle Scholar
  21. Shan, Y., McKay, R. I., & Baxter, R. (2004). Grammar model-based program evolution. In Proceedings of the 2004 IEEE Congress on evolutionary computation, 20–23 June, Portland, Oregon (pp. 478–485). Google Scholar
  22. Smith, R. (2006). Open dynamics engine. Available at http://q12.org/od.
  23. Takamura, S., Hornby, G. S., Yamamoto, T., Yokono, J., & Fujita, M. (2000). Evolution of dynamic gaits for a robot. In Proceedings of the IEEE international conference on consumer electronics, Los Angeles (pp. 192–193). Google Scholar
  24. Tanev, I. (2006). Interactive learning of mutation strategies in genetic programming. In Proceedings of the 5th joint symposium between Chonnam National University and Doshisha University, Kwangju, Korea (pp. 83–87). Chonnam: Chonnam University Press. Google Scholar
  25. Tanev, I., & Ray, T. (2005). Evolution of sidewinding locomotion of simulated limbless, wheelless robots. Artificial Life and Robotics, 9, 117–122. CrossRefGoogle Scholar
  26. Tanev, I., Ray, T., & Buller, A. (2005). Automated evolutionary design, robustness and adaptation of sidewinding locomotion of simulated snake-like robot. IEEE Transactions on Robotics, 21, 632–645. CrossRefGoogle Scholar
  27. Wong, M. L. (2005). Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines 6, 421–455. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Information Systems DesignDoshisha UniversityKyotanabeJapan

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