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Solution for a Five Link Industrial Robot Manipulator Inverse Kinematics Using Intelligent Prediction Response Method


  • Department of Mechanical Engineering Agni College of Technology, Thalambur, Off OMR, Chennai – 603103, Tamil Nadu, India
  • Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil - 629 180, Thuckalay, Kanyakumari, Tamil Nadu, India
  • Department of EEE, Noorul Islam Centre for Higher Education, Kumaracoil - 629 180, Thuckalay, Kanyakumari, Tamil Nadu, India


Background: Robot kinematics suggests and interprets the relationship exists between the kinematic position connectivity and acceleration of each link. In any of the robot manipulator the kinematics solution may be forward kinematics or inverse kinematics. To determine the joint values for a provided desired end effector orientation and position, the inverse kinematics principle is applied. Inverse kinematics is the usage of kinematics equations of a robot to find out the joint parameters that gives a targeted position of the end-effector. Methods/Statistical Analysis: In this paper, inverse kinematic solution for a five joint robot involving intelligent prediction response method has been developed and the result will be analyzed based on the performance. The intelligent prediction response method will give the performance based result, which shows the five various angles of an industrial robot. The single variation in the joint angle will be analyzed for every joint angles. This method based inverse kinematics solution is much more useful in real-time adaptive robot control where shorter calculation times are required. Findings: The MATLAB 13.0 is used to find the solution for a set of joint parameters. The actual reading and MATLAB program was found acceptable level. Applications/Improvements: To solution provided for the inverse kinematics problem with number of joint angles using the intelligent prediction response method such as artificial neural network is used.


ANN, Inverse Kinematics, Position, Robot Control.

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  • Zang Y, Tan Z, Chen K, Yang Z, Xuanijiao L. Repetitive motion of redundant robots planned by three kinds of recurrent NN and illustrated with a four-link planar manipulator’s straight line example. Robotics and Autonomous Systems. 2009; 57(6–7):645–51.
  • Koker R, Oz C, Cakar T, Ekiz H. A study of neural network based inverse kinematics solution for a three joint robot. Robotics and Autonomous Systems. 2004; 49(3–4):227–34.
  • Mirkhani M, Forsati R, Mohammed Shari A, Moayedikia A. A novel efficient algorithm for mobile robot localization. Robotics and Autonomous Systems. 2013; 61(9):920–31.
  • Chen PCY, Mills JK, Smith KC. Performance improvement of robot continuous path operation through iterative learning using neural networks. Journal of Machine Learning. 1996; 23(2):191–20.
  • Clark CM, Mills JK. Robotic system sensitivity to neural network learning rate: Theory, simulation, and experiments. International Journal Robotics Research. 2000; 19(10):955–68.
  • Arras KO, Castellanos JA, Siegwart R. Feature based multi hypothesis localization and tracking for mobile robots using geometric constraints. Proceeding of the International Conference on Robotics and Automation (ICRA-02). Washington DC, USA. 2002. p. 1371–77.
  • Duffy J. Analysis of mechanism and robot manipulators. New York: Wiley; 1980.
  • Featherstone R. Position and velocity transformation between robot end effector coordinate and joint angle. International Journal Robotics Research. 1983; 2(2):35–45.
  • Galicki M. Path constrained control of a redundant manipulator in a task space. Robotics and Autonomous Systems. 2006; 54(3):234–43.
  • Padhy NP, Simon SP. Soft computing with MATLAB Programming. New Delhi: Oxford University Press; 2015.


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