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

Date

2008-10-28

Author

BAYAR, GÖKHAN
Konukseven, Erhan İlhan
Koku, Ahmet Buğra

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

239
views

0
downloads

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.

Subject Keywords

Mobile robot, Neural networks, Radial basis function, Reinforcement learning

URI

https://hdl.handle.net/11511/52707

Collections

Department of Mechanical Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Control of a differentially driven mobile robot using radial basis function based neural networks Bayar, Gökhan; Konukseven, Erhan İlhan; Buǧra Koku, A. (2008-12-01) This paper proposes the use of radial basis function neural networks approach to the solution of a mobile robot orientation adjustment using reinforcement learning. In order to control the orientation of the mobile robot, a neural network control system has been constructed and implemented. Neural controller has been charged to enhance the control system by adding some degrees of award. Making use of the potential of neural networks to learn the relationships, the desired reference orientation and the error...
Dynamic gait pattern generation with reinforcement learning Erden, Mustafa Suphi; Leblebicioğlu, Mehmet Kemal (2005-01-01) This paper presents the gait pattern generation work performed for the sixlegged robot EA308 developed in our laboratory. The aim is to achieve a dynamically developing gait pattern generation structure using reinforcement learning. For the six legged robot a simplified simulative model is constructed. The algorithm constructs a radial basis function neural network (RBFNN) to command proper leg configurations to the simulative robot. The weights of the RBFNN are learned using reinforcement learning. The dev...
Simple and complex behavior learning using behavior hidden Markov model and CobART Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Yavaş, Mustafa (2013-03-01) This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categoriz...
Representing temporal knowledge in connectionist expert systems Alpaslan, Ferda Nur (1996-09-27) This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Position estimation for timing belt drives of precision machinery using structured neural networks KILIÇ, Ergin; DOĞRUER, CAN ULAŞ; Dölen, Melik; Koku, Ahmet Buğra (2012-05-01) This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a re...

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.