A data-driven iterative learning algorithm for robot kinematics approximation

Huu Thiet Nguyen, Chien Chern Cheah, Kar Ann Toh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an iterative learning algorithm for functional approximation, with applications to the robot kinematics problems. Various approaches have been proposed in the literature to approximate the kinematic models of robots. However, most of them assume that either the kinematic parameters or the kinematic structures of the robots are known. Neural network (NN) has been known for its inherent functional approximation capability and can be used to approximate the models when the structures of the robots are unknown. Most of these NN methods are formulated as gradient-based learning algorithms and there is no theoretical analysis to ensure convergence. Our proposed method in this paper does not require any computation of the gradient of the cost function or the inverse matrix. The convergence of the algorithm is guaranteed by theoretical analysis. The performance of the algorithm is illustrated by using a radial basis function (RBF) neural network to approximate the kinematic models of two different robots.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1031-1036
Number of pages6
ISBN (Electronic)9781728124933
DOIs
Publication statusPublished - 2019 Jul
Event2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
Duration: 2019 Jul 82019 Jul 12

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2019-July

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
CountryChina
CityHong Kong
Period19/7/819/7/12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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  • Cite this

    Nguyen, H. T., Cheah, C. C., & Toh, K. A. (2019). A data-driven iterative learning algorithm for robot kinematics approximation. In Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 (pp. 1031-1036). [8868530] (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2019.8868530