Stewart Robotic Platform for Topographic Measuring System

Carlos Hernández-Santos*, Donovan S. Labastida, Ernesto Rincón, A. Fernández-Ramírez, Fermín C. Aragón, José Valderrama-Chairez

*Corresponding author for this work

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

Abstract

In this work, a prototype of an autonomous topographic metrology system that uses a self-leveling Stewart platform system is presented, with the objective of evaluating the angular uncertainty of the azimuth plane adjustment process, which would disperse an associated collimation instrument. The self-leveling process of the prototype is achieved by means of two mutually independent four-bar kinematic chains that regulate the inclination of the platform on the “x” and “y” axes. The control of the prototype is based on the regulation of the motive source, of the kinematic chains, by means of a servomotor coupled to one of the fixed articulations and an accelerometer. The comparison of the angular error of adjustment of the calculated azimuth plane and that measured independently in an array of orthogonal toroid levels shows that the error changes as a function of the initial disturbed position and converges to a fixed value that depends on the accuracy of the source controller motor, the resolution in the range of the sensor and the alignment of the links and articulations of the kinematic chain.

Original languageEnglish
Title of host publicationAdvances in Soft Computing - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Proceedings
EditorsLourdes Martínez-Villaseñor, Ildar Batyrshin, Antonio Marín-Hernández
PublisherSpringer
Pages633-645
Number of pages13
ISBN (Print)9783030337483
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event18th Mexican International Conference on Artificial Intelligence, MICAI 2019 - Xalapa, Mexico
Duration: 27 Oct 20192 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11835 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Mexican International Conference on Artificial Intelligence, MICAI 2019
Country/TerritoryMexico
CityXalapa
Period27/10/192/11/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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