Technical Note: \cal Q-Learning
Machine Learning
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Robot Motion Planning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Motion control for mobile robot obstacle avoidance and navigation: a fuzzy logic-based approach
Systems Analysis Modelling Simulation
The Ant Algorithm for Solving Robot Path Planning Problem
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
Engineering Applications of Artificial Intelligence
Similarity of learned helplessness in human being and fuzzy reinforcement learning algorithms
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 0.00 |
This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method endows the robot with the capabilities of obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with some previous works are provided.