Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
Belief revision with reinforcement learning for interactive object recognition
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Evolution of Fuzzy Controllers for Robotic Vehicles: The Role of Fitness Function Selection
Journal of Intelligent and Robotic Systems
Expert Systems with Applications: An International Journal
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A novel hybrid learning technique applied to a self-learning multi-robot system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A new mobile robot navigation method using fuzzy logic and a modified Q-learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An overview on soft computing in behavior based robotics
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Self-learning fuzzy logic controllers for pursuit-evasion differential games
Robotics and Autonomous Systems
A reinforcement learning based radial-bassis function network control system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.