Performance Prediction Network for Serial Manipulators Inverse Kinematics Solution Passing Through Singular Configurations

Ali T Hasan,H.M.A.A Al-Assadi

doi:10.5772/10492

Abstract

This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution.Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility and backlashes in gear trainThe generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six DOF serial robot manipulator, finally the obtained results have been verified experimentally.

Highlights

The most frequently attempted to be solved problem for serial robots is the Inverse Kinematics (IK) task
It is not possible to formulate a mathematical model that has a clear mapping between Cartesian space and joint space for inverse kinematics problem, to overcome this problem, Artificial Neural Networks (ANN) uses samples to obtain the nonlinear model of such systems
Model‐based methods for solving the IK problem are inadequate if the structure of the robot is complex, ; techniques mainly based on inversion of the mapping established between the joint space and the task space of the manipulator’s Jacobian matrix have been proposed for those structures that cannot be solved in closed form

Summary

Introduction

The most frequently attempted to be solved problem for serial robots is the Inverse Kinematics (IK) task. It is not possible to formulate a mathematical model that has a clear mapping between Cartesian space and joint space for inverse kinematics problem, to overcome this problem, Artificial Neural Networks (ANN) uses samples to obtain the nonlinear model of such systems. Their ability to learn by example makes artificial neural networks very flexible and powerful when traditional model‐based modeling techniques break down. There are always kinematics uncertainties presence in the real world such as ill‐defined linkage parameters, links flexibility and backlashes in gear train, in this approach, this is very difficult in practice (Hornik, 1991), training data were recorded experimentally from sensors fixed on each joint, and the Euler (RPY) representation was used to represent the orientation, as was recommended by (Karilk and Aydin, 2000), (as they have used the robot model to get the training data and used the homogeneous transformation matrix representation to represent the orientation), the resulting network was compared to another network where the Jacobian Matrix was considered (to show the effect of singularities) , the approach was experimentally verified using a six DOF serial robot

Inverse Kinematics for Serial Manipulators

Implementing the ANN

Conclusions and Recommendations