Minimum Jerk Trajectory Planning of PUMA560 with Intelligent Computation using ANN

Abstract

Recently, robot manipulators plays very important role in manufacturing industry automation especially in assembly, pick and place operations and their performance will be improved by leveraging a proper trajectory planning. Non-linearity of the robot manipulator variables is one of the major challenges faced by the robot manipulator, which leads to more vibrations and jerk in the robot. So, in this paper, an algorithm is developed to obtain an optimal trajectory planning for robot manipulator with minimal jerk. A pick and place industrial robot PUMA-560 with six degrees of freedom is considered in this study for implementing optimization algorithm. The computational complexity of inverse kinematics in trajectory planning can be solved by using artificial neural network. To achieve the smooth trajectory, synchronized S-curve is used in this work.