Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
An experimental study on evolutionary reactive behaviors for mobile robots navigation
Keywords:
Evolutionary Robotics, Evolutionary Neural Networks, Robotic Adaptability, Simulated Robotic AgentsAbstract
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as Evolutionary Robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was also analyzed. As a result, controlled mutation do not exhibit major advantages against over the non controlled one, showing that diversity is more powerful than controlled adaptation.
Downloads
Download data is not yet available.
References
[1] D. O. Hebb. A Textbook of Psychology. Philadelphia, PA., W. B. Saunders, 1958, pp 44-45.
[2] A. L. Nelson; E. Grant; J. M. Galeotti; S. Rhody. Maze exploration behaviors using an integrated evolutionary robotic environment. Robotic and Autonomous Systems 46. 2004, pp. 159-173.
[3] Nolfi S. and Floreano, D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MA: MIT Press/Bradford Books. 2000.
[4] Elio Tuci, Inman Harvey, and Matt Quinn. Evolving integrated controllers for autonomous learning robots using dynamic neural networks. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAP’02), 4-9 August 2002, Edimburgh, UK.
[5] Lipson, H. Uncontrolled Engineering: A review of Nolfi and Floreano’s Evolutionary Robotics. 2000.
[6] Urzelai, J. and Floreano, D. Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutionary Computation, 9:495-524. 2001.
[7] E. Tuci, M. Quinn. Behavioural plasticity in autonomous agents: a comparison between two types of controller. Proceedings of The Second European Workshop on Evolutionary Robotics EvoROB2003, 14-16 April 2003, Essex, UK, pp. 661-672.
[8] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach, Prentice-Hall, Inc. 1995.
[9] D. Cliff; I. Harvey, and P. Husbands. Explorations in Evolutionary Robotics. Adaptive Behavior, 2:73-110. 1993.
[10] Nolfi, S. Adaptation as a more powerful than decomposition and integration: Experimental evidences from evolutionary robotics. In P. K. Simpson (Ed.), Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’98), New York: IEEE Press, 141-146. 1998.
[11] D. Floreano. Ago Ergo Sum. In Mulhauser, G. Editor, Evolving Consciousness. Benjamins Press, New York. 1997.
[12] L. Steels. Building agents out of autonomous behavior systems. In L. Steels and R. Brooks, editors. The Artificial Life route to Artificial Intelligence building situated embodied agents, pp. 102-137. Lawrence Erlbaum, New Haven, 1993.
[13] C. Fernando. Accumulation of Adaptations in Plastic Neural Networks. MSc. Dissertation. COGS, University of Sussex. 2002.
[14] D. Floreano and F. Mondada. Evolutionary Neurocontrollers for autonomous Mobile Robots. Neural Networks, 11:1461-1478, 1998.
[15] J. A. Driscoll, R. A. Peters II. A development environment for evolutionary robotics. Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol., pp. 3841-3845. 2000.
[16] D. Floreano and J. Urzelai. Neural morphogenesis, synaptic plasticity, and evolution. Theory in Biosciences, 120 (3-4), 223-238. 2001.
[17] S. Nolfi and D. Floreano. Learning and evolution. Autonomous Robots, 7(1): 89-113, 1999.
[18] Floreano, D. and Urzelai, J. (1999) Evolution of Adaptive-Synapse Controllers. In D. Floreano et al. (Eds.), Advances in Artificial Life. Proceedings of the 5th European Conference on Artificial Life, Berlin: Springer Verlag. (ECAL'1999). 1999.
[19] Togelius, J. Evolution of th Layers in a Subsumption Architecture Robot Controller. Master of Science in Evolutionary and Adaptive Systems. University of Sussex, UK. 2003.
[20] D. E. Goldberg. Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley. 1989.
[21] Braitenberg, V. Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press. 1984.
[22] Karlson, J. YAKS Yet Another Khepera Simulator. URL: http://r2d2.ida.his.se/ . 2002.
[23] Fernández León, J. A.; Tosini, M.; Acosta, G. G. Evolutionary Reactive Behavior for Mobile Robots Navigation. IEEE Conference on Cybernetics and Intelligent Systems (CIS). Proceedings of the 2004 IEEE CIS, Singapore, pp. 532-537. 2004.
[24] Fernández León, J. A.; Acosta, G. G.; Mayosky, Miguel A. Estudio de Neuro-Controladores Evolutivos para Navegación de Robots Autónomos. Maestría en Ingeniería de Sistemas. UNCPBA, Argentina.2005.
[25] Khepera, mini robot. K-Team. http://www.k-team.com/robots/khepera /index.html. 2004
[2] A. L. Nelson; E. Grant; J. M. Galeotti; S. Rhody. Maze exploration behaviors using an integrated evolutionary robotic environment. Robotic and Autonomous Systems 46. 2004, pp. 159-173.
[3] Nolfi S. and Floreano, D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MA: MIT Press/Bradford Books. 2000.
[4] Elio Tuci, Inman Harvey, and Matt Quinn. Evolving integrated controllers for autonomous learning robots using dynamic neural networks. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAP’02), 4-9 August 2002, Edimburgh, UK.
[5] Lipson, H. Uncontrolled Engineering: A review of Nolfi and Floreano’s Evolutionary Robotics. 2000.
[6] Urzelai, J. and Floreano, D. Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutionary Computation, 9:495-524. 2001.
[7] E. Tuci, M. Quinn. Behavioural plasticity in autonomous agents: a comparison between two types of controller. Proceedings of The Second European Workshop on Evolutionary Robotics EvoROB2003, 14-16 April 2003, Essex, UK, pp. 661-672.
[8] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach, Prentice-Hall, Inc. 1995.
[9] D. Cliff; I. Harvey, and P. Husbands. Explorations in Evolutionary Robotics. Adaptive Behavior, 2:73-110. 1993.
[10] Nolfi, S. Adaptation as a more powerful than decomposition and integration: Experimental evidences from evolutionary robotics. In P. K. Simpson (Ed.), Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’98), New York: IEEE Press, 141-146. 1998.
[11] D. Floreano. Ago Ergo Sum. In Mulhauser, G. Editor, Evolving Consciousness. Benjamins Press, New York. 1997.
[12] L. Steels. Building agents out of autonomous behavior systems. In L. Steels and R. Brooks, editors. The Artificial Life route to Artificial Intelligence building situated embodied agents, pp. 102-137. Lawrence Erlbaum, New Haven, 1993.
[13] C. Fernando. Accumulation of Adaptations in Plastic Neural Networks. MSc. Dissertation. COGS, University of Sussex. 2002.
[14] D. Floreano and F. Mondada. Evolutionary Neurocontrollers for autonomous Mobile Robots. Neural Networks, 11:1461-1478, 1998.
[15] J. A. Driscoll, R. A. Peters II. A development environment for evolutionary robotics. Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol., pp. 3841-3845. 2000.
[16] D. Floreano and J. Urzelai. Neural morphogenesis, synaptic plasticity, and evolution. Theory in Biosciences, 120 (3-4), 223-238. 2001.
[17] S. Nolfi and D. Floreano. Learning and evolution. Autonomous Robots, 7(1): 89-113, 1999.
[18] Floreano, D. and Urzelai, J. (1999) Evolution of Adaptive-Synapse Controllers. In D. Floreano et al. (Eds.), Advances in Artificial Life. Proceedings of the 5th European Conference on Artificial Life, Berlin: Springer Verlag. (ECAL'1999). 1999.
[19] Togelius, J. Evolution of th Layers in a Subsumption Architecture Robot Controller. Master of Science in Evolutionary and Adaptive Systems. University of Sussex, UK. 2003.
[20] D. E. Goldberg. Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley. 1989.
[21] Braitenberg, V. Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press. 1984.
[22] Karlson, J. YAKS Yet Another Khepera Simulator. URL: http://r2d2.ida.his.se/ . 2002.
[23] Fernández León, J. A.; Tosini, M.; Acosta, G. G. Evolutionary Reactive Behavior for Mobile Robots Navigation. IEEE Conference on Cybernetics and Intelligent Systems (CIS). Proceedings of the 2004 IEEE CIS, Singapore, pp. 532-537. 2004.
[24] Fernández León, J. A.; Acosta, G. G.; Mayosky, Miguel A. Estudio de Neuro-Controladores Evolutivos para Navegación de Robots Autónomos. Maestría en Ingeniería de Sistemas. UNCPBA, Argentina.2005.
[25] Khepera, mini robot. K-Team. http://www.k-team.com/robots/khepera /index.html. 2004
Downloads
Published
2005-12-01
How to Cite
Fernández León, J. A., Tosini, M. A., Acosta, G., & Acosta, N. (2005). An experimental study on evolutionary reactive behaviors for mobile robots navigation. Journal of Computer Science and Technology, 5(04), p. 183–188. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/834
Issue
Section
Original Articles
