Training Robot Arm 5 Degree of Freedom for Tracking the desired route using MLP

Abstract = 307 times | PDF = 220 times

Main Article Content

Zahed Kamangar Soran Saeed Asrin Zardoie


This paper work presents a new method of controlling the robot arm. The control system is the most important part of industrial robot. In industrial robot arms, it is very important to control the desired path and direction. In this paper, the presented control method is a multilayer neural network. Which controls and compares the location of the joins at the end point of the path relative to the zero position (the beginning of the path-static state). And try to learn the ultimate position of each joints due to changes in angles and direction of movement to carry out the motion process. The superiority of this method is that it can operate without considering 3D space (working space), the dynamic equations, and have Cartesian coordinates of the points on the desired path. Innovating this method of controlling the choice of the route is based on feedback from the vision system and human intelligence. This way, the operator selects and applies how to move the joints and the links of the robot and the method of walking the path. Applying the path through the movement of links and motion of joints and changing their angles in order to reach the end effector to the end point of the path. In this system, using the potentiometers (volumes) as an encoder connected to the axis of the joints, it is possible to obtain the location of the joints on the basis of variations in the voltage range and convert it to the equivalent digital 1024-0 values as has been used the MLP neural network input.


Joints, MLP, Degree of freedom, End effector, Zero position, Encoder.


Download data is not yet available.

Article Details


[1]. Edited by Rudolf Traub-Merz. (2017)Published by: Friedrich-Ebert-Stiftung, Hiroshimastraße 28, 10785 Berlin, and ISBN: 978-3-95861-597-7 [Online] available from:
[2]. T. SALIH, OMAR and YEHEA O., (2014). “an alternative solution for controlling robotic arm using FPAA technology” [Online] available from:
[3]. Y. Asano, et. al. (2012). ““Lower thigh design of detailed musculoskeletal humanoid
[4]. "Kenshiro"”, In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems
[5]. IROS, pages 4367–4372, Oct. 2012. ISBN 978-1-4673-1736-8. doi: 10.1109/IROS.2012. 6386225.
[6]. K. Hosoda, et. al. (2012). “Anthropomorphic Muscular-Skeletal Robotic Upper Limb for Understanding Embodied Intelligence”. Advanced Robotics, 26(7):729–744.
[7]. M. Jäntsch, et. al. (2013).” Anthrob – A Printed Anthropomimetic Robot”. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids).
[8]. K. Kaltsoukalas, et al., (April 2015). “Robotics and Computer-Integrated Manufacturing On generating the motion of industrial robot manipulators”, Elsevier (Robotics and Computer-Integrated Manufacturing Volume 32, Pages 65-71.
[9]. F. Nadi, Vali Derhami, Mehdi Rezaeian, (Summer 2014). “Vision Based Robot Manipulator Control with Neural Modeling of Jacobian Matrix matrix estimation”, Journal of Control, Vol. 8, No. 2.
[10]. S.M. Ahmadi, M.M. Fateh, (2014). “Robust control of electrically driven robots using control system”, Journal of Solid and Fluid Mechanics, 4(3) 11-21, 2014 (In Persian). ..... [88] M. M. Fateh, Variable structure fuzzy-linear force control of robot, Journal of.
[11]. A. Vijayan*Amrita School of Biotechnology , Hareesh Singanamala*Amrita School of Biotechnology IEEE 2013 Asha Vijayan, Chaitanya Nutakki, Chaitanya Medini, Hareesh Singanamala, Dr. Bipin G. Nair, Krishnasree Achuthan, and Dr. Shyam Diwakar, (2013)., “Classifying Movement Articulation for Robotic Arms via Machine Learning”, Journal of Intelligent Computing, IEEE, vol. 4, no. 3, pp. 123-134.
[12]. A. Vijayan, Chaitanya Nutakki, Dhanush Kumar, Dr. Krishnashree Achuthan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, (2017)., “Enabling a freely accessible open source remotely controlled robotic articulator with a neuro-inspired control algorithm”, International Journal of Interactive Mobile Technologies, vol. 13, no. 1, pp. 61-75.
[13]. P.K.Panigrahi, Saradindu Ghosh, Dayal R Parhi, (June 2014)., “Intelligent Leaning and Control of Autonomous Mobile Robot using MLP and RBF based Neural Network in Clustered Environment”, International Journal of Scientific & Engineering Research, Volume 5, Issue 6.