Abstract
In this paper we analyze entropy based measures in various motivation and environmental configurations of mobile robot navigation in complex environments. These entropy based measures are used to probe and predict various environmental and robot configurations that can provide for the emergence of highly fit robotic behaviors. The robotic system uses a neural network to evaluate measurements from its sensors in order to establish its next behavior. Genetic algorithms, fuzzy based fitness and Action-based Environment Modeling (AEM) all take a part toward training the robot. The research performed shows the utility of using these entropy based measures toward providing the robot with good training conditions.
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
Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)
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)
Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R.: Robots Meet Humans - Interacion in Public Spaces. IEEE Transactions on Industrial Electronics 52(6), 1530–1546 (2006)
Arredondo, T., Freund, W., Muñoz, C., Navarro, N., Quirós, F.: Learning Performance in Evolutionary Behavior Based Mobile Robot Navigation. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 811–820. Springer, Heidelberg (2007)
Huitt, W.: Motivation to learn: An overview. Educational Psychology Interactive (2001), http://chiron.valdosta.edu/whuitt/col/motivation/motivate.html
Tan, K.C., Goh, C.K., Yang, Y.J., Lee, T.H.: Evolving better population distribution and exploration in evolutionary multi-objective optimization. European Journal of Operations Research 171, 463–495 (2006)
Chalmers, D.J.: The evolution of learning: An experiment in genetic connectionism. In: Proceedings of the 1990 Connectionist Models Summer School, pp. 81–90. Morgan Kaufmann, San Mateo (1990)
YAKS simulator website: http://www.his.se/iki/yaks
Yamada, S.: Recognizing environments from action sequences using self-organizing maps. Applied Soft Computing 4, 35–47 (2004)
Teuvo, K.: The self-organizing map. Proceedings of the IEEE 79(9), 1464–1480 (1990)
Cover, T., Thomas, J.: Elements of Information Theory. Wiley, New York (1991)
Neelakanta, P.S.: Information-Theoretic Aspects of Neural Networks. CRC Press, Boca Raton (1999)
Handmann, U., Kalinke, T., Tzomakas, C., Werner, M., Weelen, W.v.: An image processing system for driver assistance. Image and Vision Computing 18, 367–376 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arredondo, T., Freund, W., Muñoz, C. (2008). Entropy Based Diversity Measures in Evolutionary Mobile Robot Navigation. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-69052-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69045-0
Online ISBN: 978-3-540-69052-8
eBook Packages: Computer ScienceComputer Science (R0)