Renaud Barate
ABSTRACT
We present a genetic programming system to design automatically vision based obstacle avoidance algorithms adapted to the current context. We use a simulation environment to evaluate the controllers. By restricting the structure of the algorithms to facilitate the compromise between obstacle avoidance and target reaching, we improve the generalization performance of the algorithms.
- R. Barate and A. Manzanera. Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation. From Animals to Animats 10, Proceedings of the tenth International Conference on the Simulation of Adaptive Behavior, SAB'08, To Appear, 2008. Google Scholar
Digital Library
- M. Martin. Evolving visual sonar: Depth from monocular images. Pattern Recognition Letters, 27(11):1174--1180, 2006. Google ScholarDigital Library
- A. Saxena, S. Chung, and A. Ng. 3-D Depth Reconstruction from a Single Still Image. International Journal of Computer Vision, 76(1):53--69, 2008. Google ScholarDigital Library
- P. Whigham. Grammatically-based genetic programming. Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 33--41, 1995.Google Scholar
Index Terms
- Generalization performance of vision based controllers for mobile robots evolved with genetic programming
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