Document Type : Technical Note


Mechatronics , Mechanical and Electrical Engineering , Tishreen University , Lattakia , Syria


Abstract— An educational platform is presented here for the beginner students in the Simulation and Artificial Intelligence sciences. It provides with a start point of building and simulation of the manipulators, especially of 2R planar Robot. It also displays a method to replace the inverse kinematic model (IKM) of the Robot with a simpler one, by using a Multi-Layer Perceptron Neural Network (MLP-NN), to make the end-effector able to track a specific path, which has a rectangular shape (in this article), and allocated in the robot's workspace. The method is based on Back-Propagation Levenberg Marquardt algorithm. This paper also suggests a good strategy for the simulation of the robot's motion in Matlab to tell the beginners how the operation could be done quite closely to the built-in Matlab functions. The control part was ignored here for the simplicity. So we can classify this paper as a manual in the robotic world.


Main Subjects

1.    Dorsey, N. Top 5 Robotic Systems to Watch in Agriculture. 2018  [cited 2019 19 September ]; Available from:
2.    Bouchard, S. Industrial robots: What are the different types? 2014  [cited 2019 19 September]; Available from:
3.    Household robots.  [cited 2019 19 September ]; Available from:
4.     [cited 2019 19 September]; Available from:
5.    Zhao, J., et al., A learning-based multiscale modelling approach to real-time serial manipulator kinematics simulation. Neurocomputing, 2019; Available from:
6.    Kieffer, S., V. Morellas, and M. Donath. Neural network learning of the inverse kinematic relationships for a robot arm. in Proceedings. 1991 IEEE International Conference on Robotics and Automation. 1991. IEEE.
7.    Banga, V., Y. Singh, and R. Kumar, Simulation of robotic arm using genetic algorithm & AHP. World Academy of Science, Engineering and Technology, 2007. 25(1): p. 95-101; Available from:
8.    Hao, W.G., Y.Y. Leck, and L.C. Hun. 6-DOF PC-Based Robotic Arm (PC-ROBOARM) with efficient trajectory planning and speed control. in 2011 4th International Conference on Mechatronics (ICOM). 2011. IEEE.
9.    Spong, M.W., S. Hutchinson, and M. Vidyasagar, Robot modeling and control. 2020: John Wiley & Sons.
10.    Beale, H.D., H.B. Demuth, and M. Hagan, Neural network design. Pws, Boston, 1996; Available from: