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Learning of operator hand movements via least angle regression to be teached in a manipulator

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Abstract

In this document, a control system is developed to allow a manipulator to learn and plan references from demonstrations given by an operator hand. Data entry is acquired by a sensor and is learned by the generalized learning model with least angle regression to create a desired reference in three dimensional space. A fifth reference profile is employed to smooth the desired reference. Direct and inverse kinematics are gotten to represent the transformation between the three dimensional space and each of the manipulator links. A dynamic model is gotten using Newton–Euler formulation. An evolving proportional derivative (PD) control is applied to get that the manipulator end effector follows the operator hand movements. The monitoring and control systems are implemented in an embedded platform for testing purposes.

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Acknowledgements

Authors thank the Editor in Chief, and reviewers for their valuable comments which let to improve this result. They thank the Instituto Politécnico Nacional, the Consejo Nacional de Ciencia y Tecnología, and Secretaría de Investigación y Posgrado for their support.

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Authors and Affiliations

  1. Sección de Estudios de Posgrado e Investigación, Esime Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, 02250, Mexico D.F., Mexico

    José de Jesús Rubio, Enrique Garcia, Gustavo Aquino, Jaime Pacheco & Alejandro Zacarias

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Col. San Pedro Zacatenco, 07738, Mexico D.F., Mexico

    Carlos Aguilar-Ibañez

Authors
  1. José de Jesús Rubio
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  2. Enrique Garcia
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  3. Gustavo Aquino
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  4. Carlos Aguilar-Ibañez
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  5. Jaime Pacheco
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  6. Alejandro Zacarias
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Correspondence to José de Jesús Rubio.

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Rubio, J.d.J., Garcia, E., Aquino, G. et al. Learning of operator hand movements via least angle regression to be teached in a manipulator. Evolving Systems 11, 317–332 (2020). https://doi.org/10.1007/s12530-018-9224-1

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  • DOI: https://doi.org/10.1007/s12530-018-9224-1

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