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Forward and inverse kinematics solution of a robotic manipulator using a multilayer feedforward neural network

Sharkawy Abdel-Nasser

Journal of Mechanical and Energy Engineering

2022

Vol. 6, No 2

1--17

In this paper, a multilayer feedforward neural network (MLFFNN) is proposed for solving the problem of the forward and inverse kinematics of a robotic manipulator. For the forward kinematics solution, two cases are presented. The first case is that one MLFFNN is designed and trained to find solely the position of the robot end-effector. In the second case, another MLFFNN is designed and trained to find both the position and the orientation of the robot end-effector. Both MLFFNNs are designed considering the joints’ positions as the inputs. For the inverse kinematics solution, a MLFFNN is designed and trained to find the joints’ positions considering the position and the orientation of the robot end-effector as the inputs. For training any of the proposed MLFFNNs, data is generated in MATLAB using two different cases. The first case is that data is generated assuming an incremental motion of the robot’s joints, whereas the second case is that data is obtained with a real robot considering a sinusoidal joint motion. The MLFFNN training is executed using the Levenberg-Marquardt algorithm. This method is designed to be used and generalized to any DOF manipulator, particularly more complex robots such as 6-DOF and 7-DOF robots. However, for simplicity, this is applied in this paper using a 2-DOF planar robot. The results show that the approximation error between the desired output and the estimated one by the MLFFNN is very low and it is approximately equal to zero. In other words, the MLFFNN is efficient enough to solve the problem of the forward and inverse kinematics, regardless of the joint motion type.

An investigation on the effective mass of the robot: dependence on the end-effector position

Sharkawy Abdel-Nasser

Engineering Transactions

2021

Vol. 69, iss. 3

293--313

In this paper, the mathematical analysis of the robot effective mass is presented. The calculation of this effective mass and its ellipsoid are included. The relationship between the robot effective mass and the external force (collision) affecting the robot end-effector is investigated. The effective mass is analyzed using different robot configurations and different end-effector positions. This analysis is conducted using 2-DOF and 3-DOF planar robots and executed using MATLAB. The results from this analysis prove that the robot effective mass depends on the its configurations and end-effector position. Effective mass can thus be considered as one of the criteria in optimizing robot kinematics and configuration.