Abstract
In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for a robot to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Park, H., Kim, E., Kim, H.: Robot Competition Using Gesture Based Interface. In: Hromkovič, J., Nagl, M., Westfechtel, B. (eds.) WG 2004. LNCS, vol. 3353, pp. 131–133. Springer, Heidelberg (2004)
Tasaki, T., Matsumoto, S., Ohba, H., Toda, M., Komatani, K., Ogata, T., Okuno, H.: Distance-Based Dynamic Interaction of Humanoid Robot with Multiple People. In: Hromkovič, J., Nagl, M., Westfechtel, B. (eds.) WG 2004. LNCS, vol. 3353, pp. 111–120. Springer, Heidelberg (2004)
Brooks, R.: A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation RA-2(1), 14–23 (1986)
Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)
YAKS simulator website, http://r2d2.ida.his.se/
Yamada, S.: Evolutionary behavior learning for action-based environment modeling by a mobile robot. Applied Soft Computing 5, 245–257 (2005)
Jang, J., Chuen-Tsai, S., Mitzutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997)
Konolige, K., Meyers, K., Saffiotti, A.: FLAKEY, an Autonomous Mobile Robot. SRI technical document (July 20, 1992)
Goodrige, S., Kay, M., Luo, R.: Multi-Layered Fuzzy Behavior Fusion for Reactive Control of an Autonomous Mobile Robot. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, pp. 573–578 (July 1997)
Hoffman, F.: Soft computing techniques for the design of mobile robot behaviors. Information Sciences 122, 241–258 (2000)
Al-Khatib, M., Saade, J.: An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot. Fuzzy Sets and Systems 134, 65–82 (2003)
Izumi, K., Watanabe, K.: Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots. Mathematics and Computers in Simulation 51, 233–243 (2000)
Martínez Barberá, H., Gómez Skarmeta, A.: A Framework for Defining and Learning Fuzzy Behaviours for Autonomous Mobile Robots. International Journal of Intelligent Systems 17(1), 1–20 (2002)
Zhou, C.: Robot learning with GA-based fuzzy reinforcement learning agents. Information Sciences 145, 45–68 (2002)
Seraji, H., Howard, A.: Behavior-Based Robot Navigation on Challenging Terrain: A Fuzzy Logic Approach. IEEE Trans. on Robotics and Automation 18(3), 308–321 (2002)
Huitt, W.: Motivation to learn: An overview. Educational Psychology Interactive. Valdosta State University (2001), http://chiron.valdosta.edu/whuitt/col/motivation/-motivate.html
Jang, J.-S., Sun, C.-T., Sun, M.E.: Neuro-Fuzzy and Soft Computing: a computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs (1997)
Teuvo, K.: The self-organizing map. Proceedings of the IEEE 79(9), 1464–1480 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arredondo, T.V., Freund, W., Muñoz, C., Navarro, N., Quirós, F. (2006). Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_50
Download citation
DOI: https://doi.org/10.1007/11779568_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)