Mobile Robot Heading Adjustment Using Radial Basis Function Neural Networks Controller and Reinforcement Learning

2008-10-28
This paper proposes radial basis function neural networks approach to the Solution of a mobile robot heading adjustment using reinforcement learning. In order to control the heading of the mobile robot, the neural networks control system have been constructed and implemented. Neural controller has been charged to enhance the control system by adding some degrees of strength. It has been achieved that neural networks system can learn the relationship between the desired directional heading and the error position of the mobile robot. The radial basis function neural networks have been trained via reinforcement learning function approach. The performance of the proposed controller and learning system has been investigated by using mobile robot that consists of a two driving wheels Mounted on the same axis, and a front passive wheel for balance.

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Citation Formats
G. BAYAR, E. İ. Konukseven, and A. B. Koku, “Mobile Robot Heading Adjustment Using Radial Basis Function Neural Networks Controller and Reinforcement Learning,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52707.