Abstract
We present a system that automatically selects and parameterizes a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques.
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© 2008 Springer-Verlag Berlin Heidelberg
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Barate, R., Manzanera, A. (2008). Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation. In: Asada, M., Hallam, J.C.T., Meyer, JA., Tani, J. (eds) From Animals to Animats 10. SAB 2008. Lecture Notes in Computer Science(), vol 5040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69134-1_8
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DOI: https://doi.org/10.1007/978-3-540-69134-1_8
Publisher Name: Springer, Berlin, Heidelberg
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