A Hybrid Approach in the Development of Behavior Based Robotics
Un Enfoque Híbrido en el Desarrollo de Robótica Basada en el Comportamiento
Fernando Montes González1, Carlos Alberto Ochoa Ortíz Zezzatti2, Luis Felipe Marín–Urías3 and Jöns Sánchez Aguilar4
1 Departamento de Física e Inteligencia Artificial, Universidad Veracruzana, fmontes@uv.mx
2 Instituto de Ingeniería y Tecnología (Departamento de Ingeniería Eléctrica y Computación): UACJ, megamax8@hotmail.com
]]> 3 CNRS–LAAS, Toulouse, France, lfmarin@laas.fr4 CIATEC (Centro CONACYT), León de los Aldama; México, jons_sanchez@hotmail.com
Article received on July 31, 2009.
Accepted on October 29, 2009
Abstract
In this paper we present the development of a method that combines the evolutionary robotics approach with action selection. A collection task is set in an arena where a Khepera robot has to collect cylinders that simulate food. Furthermore, two basic motivations, labeled as 'fear' and 'hunger', both affect the selection of the behavioral repertoire. In this paper we propose an initial evolutionary stage where behavioral modules are designed as separate selectable modules. Next, we use evolution for optimizing the motivated selection network employed for behavioral switching. Finally, we compare evolved selection with hand–coded selection, which offers some interesting results that support the use of a hybrid approach in the development of behavior–based robotics.
Keywords: Action Selection, Evolutionary Robotics, Behavior–Based Robotics, Bioinspired Algorithms.
]]> Resumen
En este artículo se presenta el desarrollo de un método que combina el enfoque de robótica evolutiva con el de selección de acción. De manera que en una arena se implementa una tarea de recolección para el robot Khepera que debe recoger cilindros simulando comida. Existen dos motivaciones denominadas 'miedo' y 'hambre' que afectan la selección de módulos conductuales. En este artículo se propone una etapa inicial evolutiva donde se diseñan estos módulos conductuales para que puedan ser elegibles usando selección de acción. Posteriormente se emplea evolución para optimizar la red de selección de acción. Finalmente, se comparan el ajuste de selección obtenido mediante evolución artificial y mediante un diseñador humano, favoreciendo el uso de un enfoque híbrido en el desarrollo de robótica basada en el comportamiento.
Palabras clave: Selección de Acción, Robótica Evolutiva, Robótica Basada en el Comportamiento, Algoritmos Bioinspirados.
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References
1. Arkin, R. C. (1998). Behavior–Based Robotics. Cambridge, Mass: The MIT Press. [ Links ]
2. Bares, M. & Rektor, I. (2001). Basal ganglia involvement in sensory and cognitive processing: A depth electrode CNV study in human subjects. Clinical Neurophysiology, 112(11), 2022–2030. [ Links ]
3. Brooks, R. A. (1986). A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, 2(1), 14–23. [ Links ]
4. Brooks, R. A. (1989). A robot that walks; emergent behaviors from a carefully evolved network. Neural Computation, 1(2), 253–262. [ Links ]
5. Cyberbotics (2010, February 2). Webots, Commercial Mobile Robot Simulation Software. Retrieved from http://www.cyberbotics.com [ Links ]
6. Lapchin, L. & Guillemaud, T. (2005). Asymmetry in host and parasitoid diffuse coevolution: when the red queen has to keep a finger in more than one pie. Frontiers in Zoology, 2(4). Retrieved from http://www.frontiersinzoology.com/content/2/1/4 [ Links ]
7. Maes, P. (1989). The Dynamics of Action Selection. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI–89, Detroit, MI, 991–997. [ Links ]
8. Mcfarland, D. (1994). Animal Behaviour. Harlow, Essex: Longman Scientific and Technical. [ Links ]
9. Mondada, F., Franzi, E. & Ienne, P. (1993). Mobile robot miniaturisation: a tool for investigation in control algorithms. Third International Symposium on Experimental Robotics. Lectures Notes in Control and Information Science, 200, 501–513. [ Links ]
10. Montes Gonzalez, F., Prescott, T. J., Gurney, K., Humphries, M. & Redgrave, P. (2000). An embodied model of action selection mechanisms in the vertebrate brain. In J. A. Meyer (Ed.), From Animals to Animats 6: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior, Paris, France, 157–166. [ Links ]
11. Montes–González, F. M., & Marín–Hernández, A. (2004). Central Action Selection using Sensor Fusion. In R. Baeza–Yates, J. L. Marroquín & E. Chávez (Eds.), Proceedings of the Fifth Mexican International Conference in Computer Science, ENC'04, Colima, México, 289–296. [ Links ]
12. Montes–González, F. M., Marín Hernández, A. & Ríos Figueroa, H. (2006). An Effective Robotic Model of Action Selection. 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005. Lecture Notes in Artificial Intelligence, 4177, 123–132. [ Links ]
13. Montes–González, F., Santos Reyes, J., & Ríos Figueroa, H. (2006). Integration of Evolution with a Robot Action Selection Model. 5th Mexican International Conference on Artificial Intelligence, MICAI 2006. Lecture Notes in Artificial Intelligence, 4293, 1160–1170. [ Links ]
14. Montes–Gonzalez, F., Prescott, T. J. & Negrete–Martinez, J. (2007). Minimizing human intervention in the development of basal ganglia–inspired robot control. Applied Bionics and Biomechanics, 4 (3), 101–109. [ Links ]
15. Montes–Gonzalez, F. (2007). The Coevolution of Robot Behavior and Central Action Selection. Second International Work–Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007. Lecture Notes in Computer Science, 4528, 439–448. [ Links ]
16. Montes–Gonzalez, F., Flandes–Eusebio, D., & Pellegrin–Zazueta, L. (2008). Action Selection and Obstacle Avoidance using Ultrasonic and Infrared Sensors. In Hitoshi Iba (Ed.), Frontiers in Evolutionary Robotics (327340). Vienna, Austria: I–Tech Education and Publishing. [ Links ]
17. Nolfi, S. & Floreano, D. (2000). Evolutionary Robotics. Cambridge, Mass: The MIT Press. [ Links ]
18. Prescott, T. J., Redgrave, P. & Gurney, K. (1999). Layered control architectures in robots and vertebrates. Adaptive Behaviour, 7(1), 99–127. [ Links ]
19. Prescott, T. J., Gurney, K. , Montes–Gonzalez, F., Humphries, M. & Redgrave P. (2002). The Robot Basal Ganglia: Action selection by and embedded model of the basal ganglia. In L. F. B. Nicholson & R. L. M. Faull (Eds.), Basal Ganglia VII (349–358). New York: Kluwer Academic/Plenum Press. [ Links ]
20. Prescott, T. J., Montes González, F. M., Gurney, K., Humphries, M. D. & Redgrave, P. (2006). A robot model of the basal ganglia: Behavior and Intrinsic Processing. Neural Networks, 19(1), 31–61. [ Links ]
21. Ridley, M. (2003). Evolution. (3rd ed.). Carlton, Victoria: Wiley–Blackwell. [ Links ]
22. Santos, J. & Duro, R. J. (2005). Evolución Artificial y Robótica Autónoma. Madrid: Ra–Ma Editorial. [ Links ] ]]>