Use of Learning Methods to Improve Kinematic Models

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Abstract

Kinematic model is the basic aspect in robot design and motion planning. Kinematic models are idealized, however there exist certain specific aspects of particular robot or environment, so that during navigation, the robot can significantly deviate from the planned trajectory. To increase the accuracy of motions, kinematic model can be improved and to achieve that the artificial intelligence methods can be used. In case of fixed base robots different approaches are used to train kinematics, at the same time, for the mobile base robots it proves to be a more complicated task. The reason is that a mobile robot can move unbound with respect to environment thus it is difficult to control the platform without deviation from the target position, which leads to inaccuracy in the position estimate. This paper presents the method meant for improvement of the accuracy of motion of differential drive platform. Genetic programming is used to obtain the wheel velocity function, from which the coefficient, which describes different factor influence on motion, is obtained. As a result, the kinematic model of a particular platform for a particular task is obtained. This method is effective because the developed kinematic model is more specific than the general one.

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  • [1] H. Sadjadian H.D. Taghirad “Numerical Methods for Computing the Forward Kinematics of a Redundant Parallel Manipulator” IEEEConference on Mechatronics and Robotics Aachen Germany2004 pp.557-562.

  • [2] E. Oyama S. Tachi „Modular neural net system for inverse kinematics learning” Proceedings of the 2000 IEEE Int. Conf. on Robotics andAutomation San Francisco CA USA 2000 pp 3239-3246.

  • [3] E. Oyama N.C. Young “Inverse Kinematics Learning by Modular Architecture Neural Networks with Performance Prediction Networks” IEEE International Conference on Robotics & Automation 2001 pp. 1006-1012.

  • [4] J. Martin J. Lope M. Santos “A Method to Learn the Inverse Kinematics of multi-link robots by evolving neuro-controllers” Neurocomputing vol. 72 Amsterdam The Netherlands: Elsevier 2009 pp. 2806-2814.

  • [5] B. Bosci D. Nguen-Tuong “Learning Inverse Kinematics with Structured Prediction” Intelligent Robots and Systems (IROS) 2011 pp. 698-703.

  • [6] V. R. de Angelo and C. Torras “Learning Inverse Kinematics: Reduced Sampling Through Decomposition into Virtual Robots” IEEETransactions on Systems Man and Cybernetics Part B(Cybernetics) Vol. 38 2008 pp. 1571-1577.

  • [7] G. Dutek and M. Jenkin Computational Principles of Mobile Robotics Cambridge: Cambridge University Press 2000.

  • [8] S. Kucuk Z. Bingul “Robot Kinematics: Forward and Inverse Kinematics” Industrial Robotics: Theory Modeling and Control S.Cubero Germany: Pro Literatur Verlag 2006 pp. 117-148.

  • [9] J. S. Russel P. Norwig Artificial Intelligence: A modern approach Englewood Cliffs United States of America: Prentice Hall 1995.

  • [10] Y. Singh P.K. Bhatia O.Sangwan “A review of studies on machine learning techniques” International Journal of Computer Science andSecurity Vol.1 Issue: 1 2007 pp. 70-84.

  • [11] K.R Koza. Genetic Programming: On the Programming of Computersby Means of Natural Selection Cambridge MA: MIT Press 1992.

  • [12] GPdotNET.CodePlex Documentation 2010 [Online]. Available: http://gpdotnet.codeplex.com/documentation.[Accessed: May 2 2012]

  • [13] A. Nikitenko G.Kulikovskis “Eight wheel robotic platform and its Fuzzy control system” International Conference on automation robotics and c control systems Orlando USA 2010 pp. 16-23.

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