Abstract
In this work we describe the design and implementation in a Nomad200 mobile robot of a reactive behavior aimed at wall following. A detailed analysis of the application domain has allowed us to modularize the design, conjugating in its. synthesis the potential of artificial neural networks for sensorial abstraction with other decision modules. We have carried out several experiments both in simulated and in real environments, obtaining very good results in different and unfavorable situations, which proves the robustness and flexibility of the system.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
T.L. Anderson and M. Donath, “Animal Behavior as a Paradigm for Developing Robot Autonomy”, in Designing Autonomous Agents, P. Maes (Ed.), MIT Press, pp. 145–168, 1990.
R.A. Brooks, “A Robusted Layered Control System for a Mobile Robot”, IEEE Journal Robotics and Automation, RA-2, April, pp. 14–24, 1986.
P. Maes, “Behavior-Based Artificial Intelligence”, Proc. of the 2nd. Conf. on Adaptive Behavior, MIT Press, 1993.
J.H. Conneil, “Minimalist Mobile Robotics”, Academic Press, 1990.
A. Saffioti, K. Konolige and E.H. Ruspini, “A multivalued logic approach to integrating planning and control”, Artificial Inteligence, Vol. 76, pp. 481–526, 1995.
M.C. Garcia-Alegre, P. Bustos and D. Guinea, “Complex behaviour generation on autonomous robots: A case study”, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, Vancouver, pp. 1729–1734, 1995.
J. Yen and N. Pfluger, “A Fuzzy Logic Based Extension to Payton and Rosenblatt's Comand Fusion Method for Mobile Robot Navigation”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 25, No. 6, pp. 971–978, 1995.
R.C. Arkin, “Motor Schema-Based Mobile Robot Navigation”, The International Journal of Robotics Research, Vol. 8, No. 4, pp. 92–112, 1989.
C. Scheier and R. Pfeifer, “Classification as Sensory-Motor Coordination. A Case Study on Autonomous Agents”, Proc. of the Third European Conf. on Artificial Inteligence, Berlin, Springer, pp. 657–667, 11995
S. Mahadevan and J. Connell, “Automatic Programming of Behavior-Based Robots Using Reinforcement Learning”, Artificial Intelligence, Vol. 55, pp. 311–365. 1992.
J. del R. Millan, “Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot”. Robotics and Autonomous Systems, Vol. 15, pp. 275–299, 1995.
M. Meng and A.C. Kak, “Mobile Robot Navigation Using Neural Networks and Nonmetrical Environment Models”, IEEE Control Systems, October, pp. 30–39, 1993.
J.R. Pimentel, D. Gachet, L. Moreno and M.A. Salichs, “On-line Performance Enhancement of a Behavioral Neural Network Controller”, in New Trends in Neural Computation, IWANK93, J. Mira, J. Cabestany and A. Prieto (Eds.), Springer-Verlag, pp. 694–701, 1993.
S. Nagata, M. Sekiguchi and K. Asakawa, “Mobile Robot Control by a Structured Hierarchical Neural Network”, IEEE Contr. Syst. Mag., April, pp. 69–76, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iglesias, R., Regueiro, C.V., Correa, J., Barro, S. (1997). Implementation of a basic reactive behavior in mobile robotics through artificial neural networks. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032597
Download citation
DOI: https://doi.org/10.1007/BFb0032597
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63047-0
Online ISBN: 978-3-540-69074-0
eBook Packages: Springer Book Archive