Vision-Based Autonomous Navigation with Evolutionary Learning
Por:
Moya-Albor E., Ponce H., Brieva J., Coronel S.L., Chávez-Domínguez R.
Publicada:
1 ene 2020
Resumen:
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. © 2020, Springer Nature Switzerland AG.
Filiaciones:
Moya-Albor E.:
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México, 03920, Mexico
Ponce H.:
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México, 03920, Mexico
Brieva J.:
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México, 03920, Mexico
Coronel S.L.:
Departamento de Ingeniería, Instituto Politécnico Nacional, UPIITA, Av. IPN No. 2580. Col. La Laguna Ticomán, Ciudad de México, 07340, Mexico
Chávez-Domínguez R.:
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México, 03920, Mexico
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