Self-Organizing Map Network for Wheeled Robot Movement Optimization
Authors: Boguslaw Schreyer
The paper investigates the application of the Kohonen’s Self-Organizing Map (SOM) to the wheeled robot starting and braking dynamic states. In securing wheeled robot stability as well as minimum starting and braking time, it is important to ensure correct torque distribution as well as proper slope of braking and driving moments. In this paper, a correct movement distribution has been formulated, securing optimum adhesion coefficient and good transversal stability of a wheeled robot. A neural tuner has been proposed to secure the above properties, although most of the attention is attached to the SOM network application. If the delay of the torque application or torque release is not negligible, it is important to change the rising and falling slopes of the torque. The road/surface condition is also paramount in robot dynamic states control. As the road conditions may randomly change in time, application of the SOM network has been suggested in order to classify the actual road conditions.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 369
 L. Acosta, J.A. Mendez, S. Torres, L. Moreno and G.N. Marichal, “On the Design and Implementation of a Neuromorphic Self-Tuning Controller”, Neural Processing Letters 9: 229–242, 1999
 B.J. Schreyer, “An Inquiry on 2-Mass and Wheeled Mobile Robot Dynamics”, ICMARE 2016, 18th International Conference on Mechanical, Automobile and Robotics Conference, Barcelona, Spain, August 11-12, 2016.
 B.J. Schreyer, “Dynamics of braking vehicle”, Internal Technical report. Military Academy of Technology. Warsaw, Poland, 1998
 L. Fassettt, “Fundamentals of neural networks” Prentice Hall
 B.J Schreyer, “2-Wheel mobile robot dynamics”, ICMARE Conference. Barcelone, Spain, August 11-12, 2016
 B.J Schreyer, “Efficient control of some dynamic states of wheeled robot”, MMAR Conference, Poland, Miedzyzdroje, August 28-31,2017.
 Yuan, Ya-xiang (1999). "Step-sizes for the gradient method" (PDF). AMS/IP Studies in Advanced Mathematics. 42 (2): 785.