Performance analysis for first-order configuration prediction for redundant manipulators based on avoidance manipulability

Akira Yanou, Yang Hou, Mamoru Minami, Yosuke Kobayashi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper explores a performance of first-order configuration prediction for redundant manipulators based on avoidance manipulability in order to achieve an on-line control of trajectory tracking and obstacle avoidance for redundant manipulators. In the trajectory tracking process, manipulator is required to keep a configuration with maximal avoidance manipulability in real time. Predictive control in this paper uses manipulators' future configurations to control current configuration aiming at completing tasks of trajectory tracking and obstacle avoidance on-line and simultaneously with higher avoidance manipulability. We compare Multi-Preview Control with predictive control using redundant manipulator, and show the results through simulations. The effectiveness of predictive control using first-order configuration prediction is also validated in the case of not only straight target trajectory but also curve target trajectory. In addition, an influence of measurement noise on manipulator's joint angle is newly considered.

Original languageEnglish
Pages (from-to)443-450
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume18
Issue number3
DOIs
Publication statusPublished - May 2014

Keywords

  • Avoidance manipulability
  • Configuration prediction
  • Noise environment
  • Redundant manipulators

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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