Skip to main content
SpringerLink
Log in

Evolvable hardware: A robot navigation system testbed

  • Invited Paper
  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

Recently there has been great interest in the design and study of evolvable systems based on Artificial Life principles in order to monitor and control the behavior of physically embedded systems such as mobile robots, plants and intelligent home devices. At the same time new integrated circuits calledsoftware-reconfigurable devices have been introduced which are able to adapt their hardware almost continuously to changes in the input data or processing. When the configuration phase and the execution phase are concurrent, the software-reconfigurable device is calledevolvable hardware (EHW).

This paper examines an evolutionary navigation system for a mobile robot using a Boolean function approach implemented on gate-level evolvable hardware (EHW). The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that the Boolean function approach using dedicated evolution rules is sufficient to build the desired behavior and its hardware implementation using EHW allows to decrease the learning time for on-line training. We demonstrate the effectiveness of the generalization ability of the Boolean function approach using EHW due to its representation and evolution mechanism. The results show that the evolvable hardware configuration learned off-line in a simple environment creates a robust robot behavior which is able to perform the desired behaviors in more complex environments and which is insensitive to the gap between the real and simulated world.

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

Similar content being viewed by others

References

  1. Altera Corporation,FLEX Embedded Programmable Logic Family Data Sheet, ver. 2, 1997. See http://www.altera.com.

  2. Beer, R. D. and Gallagher, J. C., “Evolving Dynamic Neural Networks for Adaptive Behavior,”Adaptive Behavior, 1, 1, pp. 91–122, July 1992.

    Article  Google Scholar 

  3. Booker, L. B., “Classifier Systems That Learn Internal World Models,”Machine Learning, 3, 2–3, pp. 161–192, 1988.

    Google Scholar 

  4. Brooks, R., “Artificial Life and Real Robots,” inProceedings of the First European Conference on Artificial Life (F. J. Varela and P. Bourgine, eds.), Cambridge, MA, 1992. MIT Press/Bradford Books, pp. 3–10, 1992.

    Google Scholar 

  5. Brooks, R., “A Robust Layered Control System for a Mobile Robot,” inProceedings of IEEE International Conference Robotics and Automation, pp. 14–23, 1986.

  6. Bruner, J. S., “Going beyond the Information Given,” inContemporary Approaches to Cognition, Boston: Harvard University Press, 1957.

    Google Scholar 

  7. Cliff, D., Harvey, I., and Husbands, P., “Explorations in Evolutionary Robotics,”Adaptive Behavior, 2, 1, pp. 73–110, July 1993.

    Article  Google Scholar 

  8. Connell, J. H. and Mahadevan, S.,Robot Learning Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publisher, 1993.

  9. de Micheli, C. and Gupta, R. K., “Hardware/Software Co-Design,”Proceedings of the IEEE, 85, 3, pp. 349–365, March 1997.

    Article  Google Scholar 

  10. Dorigo, M. and Colombetti, M., “Robot Shaping: Developing Autonomous Agents through Learning,”Artificial Intelligence, 71, pp. 321–370, 1994.

    Article  Google Scholar 

  11. Dorigo, M. and Schnepf, U., “Genetics-Based Machine Learning and Behavior-Based Robotics: A New Synthesis,”IEEE Transactions on Systems, Man, and Cybernetics, 23, 1, pp. 141–154, 1993.

    Article  Google Scholar 

  12. Floreano, D. and Mondada, F., “Automatic Creation of an Autonomous Agent: Genetic Evolution of a Neural-Network Driven Robot,” inFrom Animals to Animats 3: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior (J.-A. Meyer, d. Cliff, P. Husband, and S. Wilson, eds.), MIT Press, 1994.

  13. Goldberg, D. E.,Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989.

  14. Grefenstette, J. J., “Incremental Learning of Control Strategies with Genetic Algorithms,” inProceedings of the Sixth International Workshop on Machine Learning, Morgan Kaufmann, pp. 340–344, 1989.

  15. Grefenstette, J. J., Ramsey, C. L., and Schultz, A., “Learning Sequential Decision Rules Using Simulation Models and Competition,”Machine Learning, 5, 4, pp. 355–382, 1990.

    Google Scholar 

  16. Grefenstette, J. J. and Schultz, A., “An Evolutionary Approach to Learning in Robots,” inProceedings of the Machine Learning Workshop on Robot Learning, Eleventh International Conference on Machine Learning, New Brunswick, NJ, 1994.

  17. Harvey, I., Husbands, P., and Cliff, D., “Seeing the Light: Artificial Evolution, Real Vision,” inFrom Animals to Animats 3: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior (J.-A. Meyer, D. Cliff, P. Husbands, and S. Wilson, eds.), MIT Press, 1994.

  18. Higuchi, T., Niwa, T., Tanaka, T., Iba, H., de Garis, H., and Furuya, T., “Evolvable Hardware with Genetic Learning: A First Step towards Building a Darwin Machine,” inProceedings of the 2nd International Conference on the Simulation of Adaptive Behavior (J.-A. Meyer, H. L. Roitblat, and S. W. Wilson, eds.), MIT Press, pp. 417–424, 1992.

  19. Higuchi, T., Iwata, M., Kobayashi, A., and Yokoi, H., “Towards a Prosthetic Hand Controlled by Evolvable Hardware, inProceedings of the World Congress on Medical Physics and Biomedical Engineering (D. Geiger and P. Aletti, eds.), pp. 88, 48 rue de la Procession, 75015 Paris, France, 1997. Societe des Electriciens et Electroniciens.

    Google Scholar 

  20. Hoshino, T. and Tsuchida, M., “Manifestation of Neutral Genes in Evolving, Robot Navigation,” inArtificial Life V, Sciences of Complexity, Addison-Wesley, 1996.

  21. Husbands, P., Harvey, I., Cliff, D., and Miller, G., “The Use of Genetic Algorithms for the Development of Sensorimotor Control Systems,” inProceedings of the Third European Conference on Artificial Life (F. Moran, A. Moreno, J. J. Merelo, and P. Chacon, eds.), Granada, Spain, 1995, Springer-Verlag, pp. 110–121, 1995.

    Google Scholar 

  22. Kaelbling, L. P.,Learning in Embedded Systems, Bradford Book, MIT Press, Cambridge, 1993.

    Google Scholar 

  23. Kaelbling, L. P., “Associative Reinforcement Learning: A Generate and Test Algorithm,”Machine Learning, 15, 3, pp. 299–320, 1994.

    MATH  Google Scholar 

  24. Kaelbling, L. P., “Associative Reinforcement Learning: Functions in k-dnf,”Machine Learning, 15, 3, pp. 279–298, 1994.

    MATH  Google Scholar 

  25. Kaplan, S., “Perception of an Uncertain Environment,” inHumanscape: Environments for People, Ann Arbor, MI: Ulrich’s Books, 1982.

    Google Scholar 

  26. Koza, J., “Evolution of Subsumption Using Genetic Programming,” inProceedings of the First European Conference on Artificial Life (F. J. Varela and P. Bourgine, eds.), Cambridge, MA, 1992, MIT Press/Bradford Books, pp. 3–10, 1992.

    Google Scholar 

  27. Koza, J.Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.

  28. Lund, H. H. and Hallam, J., “Evolving Sufficient Robot Controllers,” inProceedings of the International Conference on Evolutionary Computation, Piscataway, NJ, 1997. IEEE, pp. 495–499, 1997.

    Google Scholar 

  29. Mahadevan, S., “Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions,” inProceedings of the Ninth International Conference on Machine Learning, pp. 290–299, 1992.

  30. Mataric, M. and Cliff, D., “Challenges in Evolving Controllers for Physical Robots,”Robotics and Autonomous Systems 19, 1, pp. 67–83, November 1996.

    Article  Google Scholar 

  31. Miglino, O., Lund, H. H., and Nolfi, S., “Evolving Mobile Robile Robots in Simulated and Real Environments,”Artificial Life, 2, 4, pp. 417–434, summer 1995.

    Article  Google Scholar 

  32. Murakawa, M., Yoshizawa, S., Kajitani, I., and Higuchi, T., “Evolvable Hardware for Generalized Neural Networks,” inProc. of Fifteenth International Joint Conference on Artificial Intelligence (M. E. Pollack, ed.), Morgan Kaufmann Publishers, pp. 1146–1151, 1997.

  33. Narendra, K. S. and Annaswamy, A. M.,Stable Adaptive Sysyems, Prentice Hall, Englewood Cliffs, New Jersey, 1989.

    Google Scholar 

  34. Natarajan, B. K.,Machine Learning: A Theoretical Approach, Morgan Kaufmann Publisher, 1991.

  35. Parisi, D., Nolfi, S., and Cecconi, F., “Learning, Behavior, and Evolution,” inProceedings of the First European Conference on Artificial Life, Cambridge Ma, 1992, MIT Press/Bradford Books, pp. 207–216, 1992.

    Google Scholar 

  36. Pomerleau, D. A., “Neural Network Based Autonomous Navigation,” inVision and Navigation: The CMU Navlab, Kluwer Academic Publishers, Boston, MA, 1990.

    Google Scholar 

  37. Reynolds, C. W., “An Evolved, Vision-Based Model of Obstacle Avoidance Behavior,” inArtificial Life III, Sciences of Complexity, Proc., XVII, Addison-Wesley, pp. 327–346, 1994.

  38. Salami, M., Iwata, M., and Higuchi, T., “Lossless Image Compression by Evolvable Hardware,” inProceedings of the Fourth European Conference on Artificial Life, pp. 407–416, 1997.

  39. Steels, L. and Brooks, R., eds.The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, Lawrence Erlbaum Assoc., 1995.

  40. Sutton, R. S., “Learning to Predict by the Method of Temporal Differences,”Machine Learning, 3, 1, pp. 9–44, 1988.

    Google Scholar 

  41. Sutton, R. S., “Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming,” inProceedings of the Seventh International Conference on Machine Learning, pp. 216–224, 1990.

  42. Sutton, R. S., “Special Issue on Reinforcement Learning,”Machine Learning, 8, 3/4, 1992.

  43. Thompson, A., “Evolving Electronic Robot Controllers That Exploit Hardware Resources,” inAdvances in Artificial Life: Proceedings 3rd European Conference on Artificial Life (F. Moran, A. Moreno, J. J. Merelo, and P. Chacon, eds.), Granada, Spain, 1995, Springer-Verlag, pp. 640–656, 1995.

    Google Scholar 

  44. Thrun, S.,Explanation-Based Neural Network Learning: A Lifelong Learning Approach, Kluwer Academic Publishers, Boston, MA, 1996.

    MATH  Google Scholar 

  45. Uchibe, E., Asada, M., and Hosoda, K., “Behavior, Coordination for a Mobile Robot Using Modular Reinforcement Learning,” inProceedings of IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS 96), IEEE Press, pp. 1329–1336, 1996.

  46. Valiant, L. G., “A Theory of the Learnable,”Communications of the ACM, 27, 11, pp. 1134–1142, 1984.

    Article  MATH  Google Scholar 

  47. Villasenor, J. and Mangione-Smith, W. H., “Configurable Computing,”Scientific American, pp. 66–71, 1997.

  48. Watkins, C. J. C. H. and Dayan, P., “Q-Learning,”Machine Learning, 8, 3, pp. 279–292, 1992.

    MATH  Google Scholar 

  49. Whitehead, S. D. and Ballard, D. H., “A Role for Anticipation in Reactive Systems That Learn,” inProceedings of the Sixth International Conference on Machine Learning, pp. 354–357, 1989.

  50. Wilson, S., “Classifier Systems and the Animat Problem,”Machine Learning, 2, pp. 199–228, 1987.

    Google Scholar 

  51. Xilinx Semiconductor Corporation,XC6200 Field Programmable Gate Arrays: Aduance Product Information vl.10, 1997. See http://www.xilinx.com.

Download references

Author information

Authors and Affiliations

  1. Electrotechnical Laboratory, 305, Tsukuba, Ibaraki, Japan

    Didier Keymeulen, Masaya Iwata, Kenji Konaka, Yasuo Kuniyoshi & Tetsuya Higuchi

Authors
  1. Didier Keymeulen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masaya Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenji Konaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasuo Kuniyoshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tetsuya Higuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Didier Keymeulen, Ph.D.: He currently works as a senior research engineer at the Computer Science Division of Electrotechnical Laboratory, AIST, MITI, Japan. His research interests are in the design of adaptive physically embedded systems using biologically inspired complex dynamical systems. He studied electrical and computer science engineering at the Universite Libre de Bruxelles in 1987. He obtained his M. Sc. and PH. D. in Computer Science from the Artificial Intelligence Laboratory of the Vrije Universiteit Brussel, directed by Dr. Luc Steels, respectively in 1991 and 1994. He was the Belgium laureate of the Japanese JSPS Postdoctoral Fellowship for Foreign Researchers in 1995.

Masaya Iwata, Ph.D.: He currently works as a researcher at the Computer Science Division of Electrotechnical Laboratory, AIST, MITI, Japan. His research interests are in developing adaptive hardware devices using genetic algorithms, and in their applications to pattern recognition and image compression. He received his B. E. in 1988, his M. E. in 1990, and his Ph. D. in 1993 in applied physics from the Osaka University. He was a postdoctoral fellow in optical computing at ONERA-CERT, Toulouse, France in 1993.

Kenji Konaka: He is currently working as a software research engineer at the Humanoid Interaction Laboratory of the Intelligent Systems Division of Electrotechnical Laboratory, AIST, MITI, Japan. His current research interest is on real-time vision-based mobile robots working in cooperative mode. He has developped a highly interactive distributed real-time software and hardware platform for controlling a group of robots.

Yasuo Kuniyoshi, Ph.D.: He is currently a senior research scientist and head of the Humanoid Interaction Laboratory at the Intelligent Systems Division of Electrotechnical Laboratory, AIST, MITI, Japan. His current research interest is on emergence of stable structures out of complex sensory-motor interactions by a humanoid robot. He received IJCAI93 Outstanding Paper A ward and several other awards in the field of intelligent robotics. He received the B. Eng. in applied physics in 1985, M. Eng. and Ph. D. in information engineering in 1988 and 1991 respectively, all from the University of Tokyo.

Tetsuya Higuchi, Ph.D.: He heads the Evolvable Systems Laboratory in Electrotechnical Laboratory, AIST, MITI, Japan. He received B. E., M. E., Ph. D. degrees all in electrical engineering from Keio University in 1978, 1980, and 1984, respectively. His current interests include envolvable hardware systems, parallel processing architecture in artificial intelligence, and adaptive systems. He is also in charge of the adaptive devices group in the MITI national project, Real World Computing Project.

About this article

Cite this article

Keymeulen, D., Iwata, M., Konaka, K. et al. Evolvable hardware: A robot navigation system testbed. NGCO 16, 97–122 (1998). https://doi.org/10.1007/BF03037313

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03037313

Keywords

Search

Navigation