Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Evolving ANN Controllers for Smart Mobile Robots

  • Chapter
Future Directions for Intelligent Systems and Information Sciences

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 45))

Abstract

In this work we present an overview of the application of evolution for obtaining autonomous robot controllers. It concentrates on controllers for behavior based robots implemented through Artificial Neural Networks. Specific approaches for taking into account temporal relationships are presented as well as a methodology for the progressive implementation of controllers comprising multiple behaviors. In addition we will consider the problem of transferring simulation results to real robots and the conditions that must be met for this process to be effective. Some examples of the application of these techniques to simple problems are included.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arkin, R.C. (1998), Behavior Based Robotics, MIT Press, Cambridge, MA.

    Google Scholar 

  2. Baldwin, J.M. (1896), “A New Factor in Evolution”, American Naturalist, Vol. 30, pp.441–451.

    Article  Google Scholar 

  3. Becerra, J.A. (1999), Diseno de Comportamientos de un Robot Autönomo Considerando Information Temporal, Master’s Thesis, Universidade da Coruna.

    Google Scholar 

  4. Beer, R.D. and Gallagher, J.C. (1992), “Evolving Dynamical Neural Networks for Adaptive Behavior”, Adaptive Behavior, Vol. 1, No. 1, pp. 91–122.

    Article  Google Scholar 

  5. Boers, E. and Kuiper, H. (1992), Biological Metaphors and the Design of Modular Artificial Neural Networks, Master’s Thesis, Leiden University.

    Google Scholar 

  6. Braitenberg, V. (1984), Vehicles: Experiments in Synthetic Psychology, MIT Press, Cambridge, MA.

    Google Scholar 

  7. Brooks, R. (1987), Planning is Just a Way of Avoiding Figuring Out What to Do Next, Working Paper 303, MIT AI Laboratory.

    Google Scholar 

  8. Brooks, R.A. (1990), “Elephants don’t Play Chess”, In Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back, Patti Maes (Ed.), MIT Press, pp. 3–15.

    Google Scholar 

  9. Brooks, R.A. (1991), “Intelligence without Representation”, Artificial Intelligence, Vol. 47, pp. 139–159.

    Article  Google Scholar 

  10. Brooks, R.A. (1991), “New Approaches to Robotics”, Science, Vol. 253, pp. 1227–1231.

    Article  Google Scholar 

  11. Cangelosi, A., Parisi, D., and Nolfi, S. (1994), “Cell Division and Migration in a Genotype for Neural Networks”, Network, Vol. 5, pp. 497–515.

    Article  MATH  Google Scholar 

  12. Chalmers, D.J. (1992), “Subsymbolic Computation and the Chinese Room”, Symbolic and Connectionist Paradigms: Closing the Gap, Dinsmore, J., (Ed.).

    Google Scholar 

  13. Cliff, D. and Miller, G. F. (1996), “Co-evolution of Pursuit and Evasion II: Simulation Methods and Results”, From Animals to Animats 4, MIT Press, pp. 506–515.

    Google Scholar 

  14. Cliff, D., Harvey, I., and Husbands, P. (1992), Incremental Evolution of Neural Network Architectures for Adaptive Behaviour, Tech. Rep. No. CSRP256, Brighton, School of Cognitive and Computing Sciences, University of Sussex, UK.

    Google Scholar 

  15. Cliff, D.T., Husbands, P., and Harvey, I. (1993), “Evolving Visually Guided Robots”, From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behaviour (SAP 92), J-A. Meyer, H. Roitblat and S. Wilson (Eds.), MIT Press Bradford Books, Cambridge, MA, pp. 374–383.

    Google Scholar 

  16. Dain, R.A. (1998), “Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming”, Applied Intelligence, Vol. 8, pp. 33–41.

    Article  Google Scholar 

  17. Dellaert, F. and Beer, R.D. (1996), “A Developmental Model for the Evolution of Complete Autonomous Agents”, From Animals to Animats 4, MIT Press, pp. 393–401.

    Google Scholar 

  18. Dorigo, M. and Colombetti, M. (1998), Robot Shaping: An Experiment in Behavior Engineering, MIT Press.

    Google Scholar 

  19. Duro, R.J. and Santos, J. (1999), “Bearance of Noise on Performance and Distribution of Computations Using DTB”, Ninth International Conference on Artificial Neural Networks, Edinburgh, UK.

    Google Scholar 

  20. Duro, R.J., Crespo, J. L., Santos, J. (1999), ‘Training Higher Order Gaussian Synapses”, Foundations and Tools for Neural Modeling, J. Mira & J.V. Sánchez-Andrés (Eds.), Lecture Notes in Computer Science, Vol. 1606, Springer-Verlag, Berlin, pp. 537–545.

    Chapter  Google Scholar 

  21. Floreano, D. and Mondada, F. (1996), “Evolution of Homing Navigation in a Real Mobile Robot”, IEEE Transactions on Systems, Man, and Cybernetics, Part-B, Vol. 26, pp. 396–407.

    Article  Google Scholar 

  22. Floreano, D. and Mondada, F. (1998), “Evolutionary Neurocontrollers for Autonomous Mobile Robots”, Neural Networks, Vol. 11, pp. 1461–1478.

    Article  Google Scholar 

  23. Floreano, D. and Nolfi, S. (1997), “Adaptive Behavior in Competing Co-evolving Species”, P. Husbands and I. Harvey (Eds.), Proceedings of Fourth European Conference on Artificial Life (ECAL 97), Complex Adaptive Systems Series, MIT Press, pp. 378–387.

    Google Scholar 

  24. Floreano, D. and Nolfi, S. (1997), “God Save the Red Queen!. Competition in Co-evolutionary Robotics”, J. Koza, K. Deb, M. Dorigo, D. Fogel, M. Garzon, H. Iba, and R.L. Riolo (Eds.), Proceedings of the 2nd International Conference on Genetic Programming, Stanford University, pp. 398–406.

    Google Scholar 

  25. Fogel, L.J. (1964), On the Organization of Intellect, PhD Dissertation, University of California.

    Google Scholar 

  26. Foner, L.N. and Maes, P. (1994), “Paying Attention to What’s Important: Using Focus of Attention to Improve Unsupervised Learning”, Proceedings of the third International Conference on Simulation of Adaptive Behavior (SAB94), pp. 256.

    Google Scholar 

  27. Gallagher, J.C., Beer, R.D., Espenschied, K.S., and Quinn, R.D. (1994), Application of Evolved Locomotion Controllers to a Hexapod Robot, Technical Report CES-94–7, Dept. of Computer Engineering and Science, Case Western Reserve University.

    Google Scholar 

  28. Giles, L. and Maxwell, T. (1987), “Learning Invariance and Generalization in High Order Neural Networks”, Applied Optics, Vol. 26, No. 23, pp. 4972–4978.

    Article  Google Scholar 

  29. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, New-York, Addison-Wesley.

    MATH  Google Scholar 

  30. Gomez, F. and Miikkulainen, R. (1997), “Incremental Evolution of Complex General Behavior”, Adaptive Behavior, Vol. 5, No. 3/4, pp. 317–342.

    Article  Google Scholar 

  31. Gruau, F. (1992), “Genetic Systems of Boolean Neural Networks with a Cell Rewriting Developmental Process”, Combination of Genetic Algorithms and Neural Networks, IEEE Computer Society Press.

    Google Scholar 

  32. Gruau, F. (1992), Cellular Encoding of Genetic Neural Networks, Technical Report 92–21, Laboratoire de L’Informatique du Parallelisme, Ecole Normale Superieure de Lyon.

    Google Scholar 

  33. Guillot, A. and Meyer, J.A. (1997), “Synthetic Animals in Synthetic Worlds”, In Kunii et Luciani (Eds), Synthetic Worlds, John Wiley and Sons.

    Google Scholar 

  34. Harnad, S. (1990), “The Symbol Grounding Problem”, Physica D, Vol. 42, pp. 335–346.

    Article  Google Scholar 

  35. Harvey, I. (1992), “Species Adaptation Genetic Algorithms: A Basis for a Continuing SAGA”, F. Varela and P. Bourgine (Eds.), Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life (ECAL91), MIT Press, Cambridge, MA, pp. 346–354.

    Google Scholar 

  36. Harvey, I. (1996), “Artificial Evolution and Real Robots”, Proceedings of International Symposium on Artificial Life and Robotics (AROB), Masanori Sugisaka (Ed.), Beppu, Japan, pp. 138–141.

    Google Scholar 

  37. Harvey, I., Husbands, P., and Cliff, D. (1993), “Issues in Evolutionary Robotics”, J-A. Meyer, H. Roitblat, and S. Wilson (Eds.), From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), MIT Press, Cambridge, MA, pp. 364–373.

    Google Scholar 

  38. Holland, J. (1975), Adaptation in Natural and Artificial Systems, University of Michingan Press, Ann Arbor.

    Google Scholar 

  39. Husbands, P., Harvey, I., Cliff, D., and Miller, G. (1994), “The Use of Genetic Algorithms for the Development of Sensorimotor Control Systems”, P. Gaussier and J.-D. Nicoud (Eds.), From Perception to Action, IEEE Computer Society Press, Los Alamitos CA, pp. 110–121.

    Google Scholar 

  40. Jakobi, N. (1997), “Evolutionary Robotics and the Radical Envelope of Noise Hypothesis”, Adaptive Behavior, Vol. 6, No. 2, pp. 325–368.

    Article  Google Scholar 

  41. Jakobi, N., (1997), “Half-Baked, Ad-Hoc and Noisy Minimal Simulations for Evolutionary Robotics”, Fourth European Conference on Artificial Life, P. Husbands and I. Harvey (Eds.), MIT Press.

    Google Scholar 

  42. Jakobi, N., Husbands, P., and Harvey, I. (1995), “Noise and the Reality Gap: the Use of Simulation in Evolutionary Robotics”, F. Morán, A. Moreno, J.J. Merelo and P. Cachou (Eds.), Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life (ECAL 95), Lecture Notes in Artificial Intelligence, Vol. 929, Springer-Verlag, pp. 704–720.

    Google Scholar 

  43. Kodjabachian, J. and Meyer, J-A (1995), “Evolution and Development of Control Architectures in Animats”, Robotics and Autonomous Systems, Vol. 16, pp. 161–182.

    Article  Google Scholar 

  44. Kodjabachian, J. and Meyer, J-A (1998), “Evolution and Development of Modular Control Architectures for 1-D Locomotion in Six-Legged Animats”, Connection Science, Vol. 10, No. 3–4, pp. 211–37.

    Article  Google Scholar 

  45. Kodjabachian, J. and Meyer, J-A (1998), “Evolution and Development of Neural Controllers for Locomotion, Gradient-Following, and Obstacle-Avoidance in Artificial Insects”, IEEE Transactions on Neural Networks, Vol. 9, No.5, pp. 796–812.

    Article  Google Scholar 

  46. Koza, J.R. (1989), “Hierarchical Genetic Algorithms Operating on Populations of Computer Programs”, Proc. 11th Int. Joint Conf on Artificial Intelligence, San Mateo, California.

    Google Scholar 

  47. Koza, J.R. (1994), Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, MA.

    MATH  Google Scholar 

  48. Lee, W-P., Hallam, J., and Lund, H.H. (1996), “A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks”. Proceedings of IEEE Third International Conference on Evolutionary Computation, IEEE Press.

    Google Scholar 

  49. Lund, H.H. and Hallam, J., Sufficient Neurocontrollers can Be Surprisingly Simple, Research Paper 824, Department of Artificial Intelligence, University of Edinburg, 1996.

    Google Scholar 

  50. Lund, H.H. and Miglino, O. (1998), “Evolving and Breeding Robots”, Proceedings of First European Workshop on Evolutionary Robotics, Springer-Verlag.

    Google Scholar 

  51. Lund, H.H., Miglino, O., Pagliarini, L., Billard, A., and Ijspeert, A. (1998), “Evolutionary Robotics — A Children’s Game”, Proceedings of IEEE 5th International Conference on Evolutionary Computation.

    Google Scholar 

  52. Maes, P. (1990), “A Bottom-up Mechanism for Behavior Selection in an Artificial Creature”, Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90).

    Google Scholar 

  53. Mataric, M. and Cliff, D. (1996), “Challenges in Evolving Controllers for Physical Robots”, Robotics and Autonomous Systems, Vol. 19, No. 1, pp. 67–83.

    Article  Google Scholar 

  54. Mataric, M.J. (1992), “Integration of Representation into Goal Driven Behavior Based Robotics”, IEEE Transactions on Robotics and Automation, Vol. 8, No. 3, pp. 304–312.

    Article  Google Scholar 

  55. Matellán, V., Fernández, C, and Molina, J.M. (1998), “Genetic Learning of Fuzzy Reactive Controllers”, Robotics and Autonomous Systems, Vol. 25, pp. 33–41.

    Article  Google Scholar 

  56. Meeden, L. (1996), “An Incremental Approach to Developing Intelligent Neural Network Controllers for Robots”, IEEE Transactions on Systems, Man and Cybernetics Part. B: Cybernetics, Vol. 26, No. 3, pp. 474–485.

    Article  Google Scholar 

  57. Menczer, F. and Belew, R.K. (1994), “Evolving Sensors in Environments of Controlled Complexity”, Artificial Life IV, R. Brooks and P. Maes (Eds.), Cambridge, MA, MIT Press, pp. 210–221.

    Google Scholar 

  58. Miglino, O., Lund, H.H., and Nolfi, S. (1995), “Evolving Mobile Robots in Simulated and Real Environments”, Artificial Life, Vol. 2, No. 4, pp 417–434.

    Article  Google Scholar 

  59. Miglino, O., Nafasi, K., and Taylor, C. (1995), “Selection for Wandering Behavior in a Small Robot”, Artificial Life, Vol. 2, pp. 101–116.

    Article  Google Scholar 

  60. Mondada, F. and Floreano, D. (1995), “Evolution of Neural Control Structures: Some Experiments on Mobile Robots”, Robotics and Autonomous Systems, Vol. 16, pp. 183–195.

    Article  Google Scholar 

  61. Nolfi, S. (1997), “Evolving Non-Trivial Behaviors on Real Robots: A Garbage Collecting Robot”, Robotics and Autonomous Systems, Vol. 22, pp. 187–198.

    Article  Google Scholar 

  62. Nolfi, S. (1997), “Using Emergent Modularity to Develop Control Systems for Mobile Robots”, Adaptive Behavior, Vol. 5, No. 3/4, pp. 343–363.

    Article  Google Scholar 

  63. Nolfi, S. and Parisi, D. (1997), “Learning to Adapt to Changing Environments in Evolving Neural Networks”, Adaptive Behavior, Vol. 5, No. 1, pp. 75–98.

    Article  Google Scholar 

  64. Nolfi, S., Elman, J., and Parisi, D. (1994), “Learning and Evolution in Neural Networks”, Adaptive Behavior, Vol. 1, pp. 5–28.

    Article  Google Scholar 

  65. Nolfi, S., Floreano, D., Miglino, O., and Mondada, F. (1994), “How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics”. R. Brooks and P. Maes (Eds.), Proceedings of Fourth International Conference on Artificial Life, Cambridge, MA, MIT Press.

    Google Scholar 

  66. Nordin, P., Banzhaf, W., and Brameier, M. (1998), “Evolution of a World Model for a Miniature Robot Using Genetic Programming”, Robotics and Autonomous Systems, Vol. 25, pp. 105–116.

    Article  Google Scholar 

  67. Patel, M.J., Colombetti, M., and Dorigo, M. (1995), “Evolutionary Learning for Intelligent Automation: a Case Study”, Intelligent Automation and Soft Computing Journal, Vol. 1, No. 1, pp. 29–42.

    Google Scholar 

  68. Rechenberg, I. (1965), Cybernetic Solution Path of an Experimental Problem, Library Translation 1122, Royal Aircraft Establishment, Farnborough, UK.

    Google Scholar 

  69. Reynolds, C.W. (1994), “Evolution of Corridor Following Behavior in a Noisy World”, From Animals to Animats 3, MIT Press, pp. 402–410.

    Google Scholar 

  70. Rumelhart, D.E., Mclelland, J.L. (1986), Parallel Distributed Processing 1, 2. MIT Press.

    Google Scholar 

  71. Salomon, R. (1997), ‘The Evolution of Different Neuronal Control Structures for Autonomous Agents”, Robotics and Autonomous Systems, Vol. 22, pp. 199–213.

    Article  Google Scholar 

  72. Santos, J. and Duro, R.J. (1998), “Evolving Neural Controllers for Temporally Dependent Behaviors in Autonomous Robots”, Tasks and Methods in Applied Artificial Intelligence, A.P. del Pobil, J. Mira and M. Ale (Eds.), Lecture Notes in Artificial Intelligence, Vol. 1416, Springer-Verlag, Berlin, pp. 319–328.

    Chapter  Google Scholar 

  73. Schwefel, H.P. (1975), Evolutionsstrategie und Numerische Optimierung, Ph. D. Thesis, Technische Universität Berlin.

    Google Scholar 

  74. Sharkey, N.E. (1997), ‘The new Wave in Robot Learning”, Robotics and Autonomous Systems, Vol. 22, pp. 179–185.

    Article  Google Scholar 

  75. Stone, J.V. (1995), “Evolutionary Robots: Our Hands In Their Brains?”, Artificial Life IV, pp. 400–405.

    Google Scholar 

  76. Walter, G. (1953), The Living Brain, Norton, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Grupo de Sistemas Autónomos, Universidade da Coruña, Spain

    R. J. Duro, J. Santos & J. A. Becerra

Authors
  1. R. J. Duro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. A. Becerra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Science, University of Otago, P.O. Box 56, Dunedin, New Zealand

    Nikola Kasabov

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Duro, R.J., Santos, J., Becerra, J.A. (2000). Evolving ANN Controllers for Smart Mobile Robots. In: Kasabov, N. (eds) Future Directions for Intelligent Systems and Information Sciences. Studies in Fuzziness and Soft Computing, vol 45. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1856-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1856-7_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2470-4

  • Online ISBN: 978-3-7908-1856-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation