Abstract
In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions were tuned applying a distributed evolutionary learning based on social wound treatment inspired in the Megaponera analis ant. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The results show that the optimization of the input fuzzy membership functions improves the navigation behavior against an empirical tuning of them.
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Acknowledgements
Ernesto Moya-Albor, Hiram Ponce, Jorge Brieva and Rodrigo Chávez-Domínguez would like to thank the Facultad de Ingeniería of Universidad Panamericana (Campus Mexico City) for all support in this work. Sandra L. Coronel thanks to Instituto Politécnico Nacional (UPIITA) for the support in this work.
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Moya-Albor, E., Ponce, H., Brieva, J., Coronel, S.L., Chávez-Domínguez, R. (2020). Vision-Based Autonomous Navigation with Evolutionary Learning. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_39
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