A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach
Journal
Journal of Artificial Intelligence and Technology
ISSN
2766-8649
2766-8649
Publisher
Intelligence Science and Technology Press
Date Issued
2024
Type
text::journal::journal article
Abstract
The paper presents a fuzzy Q-learning (FQL) and optical flow-based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python. ©The authors ©INTELLIGENCE SCIENCE AND TECHNOLOGY PRESS INC.
License
Acceso Abierto
How to cite
Cuesta-Solano, R., Moya-Albor, E., Brieva, J., & Ponce, H. (2024). A Vision-Based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach. Journal of Artificial Intelligence and Technology, 4(4), 363–369. https://doi.org/10.37965/jait.2024.0511