03.2021.159.171

Design of planning Trajectory for the planar Robot Manipulator using Linear Segments method with Parabolic Blends (LSPB)

Author(s):

Wisam Essmat Abdul-Lateef† Abdullah Faraj Huayier‡ Naseer H. Farhood†

Affiliation(s):

†Electromechanical Engineering Department, University of Technology, Bagdad, Iraq
‡Production Engineering & Metallurgy Departments, University of Technology, Baghdad, Iraq

Corresponding Author Email: 50110@uotechnology.edu.iq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recently, the demand for robot manipulators has been increasing so rapidly that their applications have become used across a wide range of products for example, industrial, medical, military etc. Thus, a further attention has been paid for their guidance and trajectories. As such, the selection of the optimal method of trajectory calculation and its control of planar Robot Manipulator is a very important issue. This in turns can enable the user to control on the required motion in the common space of robotic manipulator used to come to the desired points within of its workspace. Furthermore, it can achieves the best trajectory for the robot manipulator and provides a way satisfy the aims of the required working motion, including reducing time interval, prevent obstacles collision, decreasing the path traveling economic cost and achieving robot manipulator kinematic and dynamic constraints. This paper investigated the trajectory method called linear segment with parabolic blend (LSPB), to describe the segments that connect initial, intermediate, and final points at joint-space planning for a model of 2-link planar robot having two degrees of freedom. The control of trajectory is achieved by using artificial neural network (ANN) controller. The results showed that the artificial neural network control method presented good results to the required tasks according to the desired inputs with a minimum error. The equations of system were solved by using differential equations available in MATLAB and the model results are validated through simulation.