Fuzzy hint acquisition for the collision avoidance solution of redundant manipulators using neural network

Samy F.M. Assal, Keigo Watanabe, Kiyotaka Izumi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

Original languageEnglish
Pages (from-to)17-29
Number of pages13
JournalInternational Journal of Control, Automation and Systems
Volume4
Issue number1
Publication statusPublished - Feb 1 2006
Externally publishedYes

Keywords

  • Fuzzy system
  • Inverse kinematics
  • Neural networks
  • Online collision avoidance
  • Redundant manipulators

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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