Abstract
This paper focused on the problem of the autonomous mobile robot navigation under the unknown and changing environment. The reinforcement learning (RL) is applied to learn behaviors of reactive robot. T-S fuzzy neural network and RL are integrated. T-S network is used to implement the mapping from the state space to Q values corresponding with action space of RL. The problem of continuous, infinite states and actions in RL is able to be solved through the function approximation of proposed method. Finally, the method of this paper is applied to learn behaviors for the reactive robot. The experiment shows that the algorithm can effectively solve the problem of navigation in a complicated unknown environment.
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© 2008 Springer-Verlag Berlin Heidelberg
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Duan, Y., Cui, B., Yang, H. (2008). Robot Navigation Based on Fuzzy RL Algorithm. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_44
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DOI: https://doi.org/10.1007/978-3-540-87732-5_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
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