Intelligent systems based solutions for the kinematics problem of the industrial robot arms

Emre Sariyildiz, Kemal Ucak, Kouhei Ohnishi, Gulay Oke, Hakan Temeltas

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

1 Citation (Scopus)

Abstract

In this paper, three intelligent system methods namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), are implemented to solve the inverse kinematics problem of the industrial robot arms. The main advantages of the intelligent system based solutions in the robot kinematics are that they can be easily implemented in analysis of complex mechanisms and their solutions do not suffer by the singularity that is one of the fundamental problems of inverse kinematics. The screw theory and quaternion algebra based kinematic model is used to improve the model efficiency by decreasing the computational complexity and load. The kinematics problem of the Staubli TX-60L industrial robot arm is analyzed by using the proposed intelligent system based solutions and simulation results are given.

Original languageEnglish
Title of host publication2013 9th Asian Control Conference, ASCC 2013
DOIs
Publication statusPublished - 2013 Oct 31
Event2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey
Duration: 2013 Jun 232013 Jun 26

Publication series

Name2013 9th Asian Control Conference, ASCC 2013

Other

Other2013 9th Asian Control Conference, ASCC 2013
CountryTurkey
CityIstanbul
Period13/6/2313/6/26

Keywords

  • ANFIS
  • Artificial Neural Networks
  • Redundancy
  • Robot Arm
  • Singularity
  • Support Vector Machine
  • Trajectory Tracking

ASJC Scopus subject areas

  • Control and Systems Engineering

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  • Cite this

    Sariyildiz, E., Ucak, K., Ohnishi, K., Oke, G., & Temeltas, H. (2013). Intelligent systems based solutions for the kinematics problem of the industrial robot arms. In 2013 9th Asian Control Conference, ASCC 2013 [6606306] (2013 9th Asian Control Conference, ASCC 2013). https://doi.org/10.1109/ASCC.2013.6606306