A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach  

作　　者：Roberto Cuesta-Solano Ernesto Moya-Albor Jorge Brieva Hiram Ponce 

机构地区：[1]Facultad de Ingeniería,Universidad Panamericana,Augusto Rodin 498,Ciudad de México 03920,México

出　　处：《Journal of Artificial Intelligence and Technology》2024年第4期363-369,共7页人工智能技术学报（英文）

摘　　要：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.

关 键 词：CoppeliaSim evolutionary algorithm fuzzy Q-learning optical flow reinforced learning vision-based control navigation 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]