Payload estimation for a robotic system using unsupervised classification

L. Angel; J. Viola

doi:10.1109/STSIVA.2016.7743300

Payload estimation for a robotic system using unsupervised classification

L. Angel, J. Viola

Research output: Chapter in Book/Report/Conference proceeding › Conference and proceedings › peer-review

3 Scopus citations

Abstract

A robotic system may be affected by external disturbances and parametric uncertainness, which change its dynamical behavior. One of the most common disturbances is the payload variation that affects the control system performance. If the payload variation is known, its negative effects can be minimized adjusting the control system parameters. However, when the payload variation is unknown, the control system parameters cannot be adjusted appropriately. This paper proposes a methodology for the payload variation estimation for a robotic system using unsupervised classification techniques. BSAS, MBSAS and Kmeans algorithms were employed as clustering techniques. The Silhouette index and the standard deviation were employed as performance indexes to compare the classification algorithms. Results showed that Kmeans algorithm has a better performance for the payload variation classification.

Original language	English
Title of host publication	2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
Editors	Miguel Altuve
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509037971
DOIs	https://doi.org/10.1109/STSIVA.2016.7743300
State	Published - 14 Nov 2016
Event	21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 - Bucaramanga, Colombia Duration: 30 Aug 2016 → 2 Sep 2016

Publication series

Name	2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

Conference

Conference	21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
Country/Territory	Colombia
City	Bucaramanga
Period	30/08/16 → 2/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/STSIVA.2016.7743300

Cite this

Angel, L., & Viola, J. (2016). Payload estimation for a robotic system using unsupervised classification. In M. Altuve (Ed.), 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 Article 7743300 (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STSIVA.2016.7743300

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title = "Payload estimation for a robotic system using unsupervised classification",

abstract = "A robotic system may be affected by external disturbances and parametric uncertainness, which change its dynamical behavior. One of the most common disturbances is the payload variation that affects the control system performance. If the payload variation is known, its negative effects can be minimized adjusting the control system parameters. However, when the payload variation is unknown, the control system parameters cannot be adjusted appropriately. This paper proposes a methodology for the payload variation estimation for a robotic system using unsupervised classification techniques. BSAS, MBSAS and Kmeans algorithms were employed as clustering techniques. The Silhouette index and the standard deviation were employed as performance indexes to compare the classification algorithms. Results showed that Kmeans algorithm has a better performance for the payload variation classification.",

author = "L. Angel and J. Viola",

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Angel, L & Viola, J 2016, Payload estimation for a robotic system using unsupervised classification. in M Altuve (ed.), 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016., 7743300, 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, Institute of Electrical and Electronics Engineers Inc., 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, Bucaramanga, Colombia, 30/08/16. https://doi.org/10.1109/STSIVA.2016.7743300

Payload estimation for a robotic system using unsupervised classification. / Angel, L.; Viola, J.
2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016. ed. / Miguel Altuve. Institute of Electrical and Electronics Engineers Inc., 2016. 7743300 (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference and proceedings › peer-review

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N2 - A robotic system may be affected by external disturbances and parametric uncertainness, which change its dynamical behavior. One of the most common disturbances is the payload variation that affects the control system performance. If the payload variation is known, its negative effects can be minimized adjusting the control system parameters. However, when the payload variation is unknown, the control system parameters cannot be adjusted appropriately. This paper proposes a methodology for the payload variation estimation for a robotic system using unsupervised classification techniques. BSAS, MBSAS and Kmeans algorithms were employed as clustering techniques. The Silhouette index and the standard deviation were employed as performance indexes to compare the classification algorithms. Results showed that Kmeans algorithm has a better performance for the payload variation classification.

AB - A robotic system may be affected by external disturbances and parametric uncertainness, which change its dynamical behavior. One of the most common disturbances is the payload variation that affects the control system performance. If the payload variation is known, its negative effects can be minimized adjusting the control system parameters. However, when the payload variation is unknown, the control system parameters cannot be adjusted appropriately. This paper proposes a methodology for the payload variation estimation for a robotic system using unsupervised classification techniques. BSAS, MBSAS and Kmeans algorithms were employed as clustering techniques. The Silhouette index and the standard deviation were employed as performance indexes to compare the classification algorithms. Results showed that Kmeans algorithm has a better performance for the payload variation classification.

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BT - 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

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Angel L, Viola J. Payload estimation for a robotic system using unsupervised classification. In Altuve M, editor, 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7743300. (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016). doi: 10.1109/STSIVA.2016.7743300

Payload estimation for a robotic system using unsupervised classification

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