An Effective Prediction of Position Analysis of Industrial Robot Using Fuzzy Logic Approach

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


Industrial robots have been extensively used by many industries as well as organizations for different applications. This paper introduces some qualitative parameters to find out the best predictive value as per the comparison to experimental value. In the fuzzy-based method, the weight of each criterion and the rating of each alternative are described by using different membership functions and linguistic terms. By using four techniques triangular, trapezoidal, Gaussian and Hybrid membership functions, the effective prediction of robot angle with their position is determined in accordance to space of Robot. This paper compares four techniques and found that the Hybrid membership function is the effective one for determination of effective prediction measurement as it shows a good agreement with the experimental result. By taking help of this paper the user can easily access to any point of the workspace locations. It is very effective one by the fuzzy logic systems to analyze the work space.


Six axis industrial robot Fuzzy logic Triangular Trapezoidal Gaussian Hybrid membership function 


  1. 1.
    Hwang, J.Y., Kim, J.S., Lim, S.S., Park, K.H.: A fast path planning by path graph optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans 33(1), 121–128 (2003)Google Scholar
  2. 2.
    Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Elsevier Mech. Mach. Theory 42, 455–471 (2007)MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Bhalerao, K.D., Critchley, J., Anderson, K.: An efficient parallel dynamics algorithm for simulation of large articulated robotic systems. Elsevier Mech. Mach. Theory 53, 86–98 (2012)CrossRefGoogle Scholar
  4. 4.
    Srikanth, A., Sravanth, M., Sreechand, V., Kumar, K.K.: Kinematic analysis of 3 DOF of serial robot for industrial applications. Int. J. Eng. Trends Technol. 4(4), 1000–1004 (2013). ISSN: 2231–5381Google Scholar
  5. 5.
    Kazar, O., Ghodbane, H., Moussaoui, M., Belkacemi, A.: A multi-agent approach based on fuzzy logic for a robot manipulator. Int. J. Digital Content Technol. Appl. 3(3), 86–90 (2009)Google Scholar
  6. 6.
    Abd, K., Abhary, K., Marian, R.: Application of fuzzy logic to multi-objective scheduling problems in robotic flexible assembly cells. Autom. Control Intell. Syst. 1(3), 34–41 (2013)CrossRefGoogle Scholar
  7. 7.
    Ahmad, M.A., Tumari, M.Z.M., Nasir, A.N.K.: Composite fuzzy logic control approach to a flexible joint manipulator. Int. J. Adv. Rob. Syst. 10(58), 1–9 (2013)Google Scholar
  8. 8.
    Das, H., Parhi, D.R.: Application of neural network for fault diagnosis of cracked cantilever beam. World Congress on Nature and Biologically Inspired Computing, pp. 1303–1308 (2009)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIGIT SarangDhenkanalIndia

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