Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—While inverse kinematics is used as a trajectory generator for the path tracking of the end effector of robots, precise control of terminal device is difficult due to accumulated tracking errors. Therefore, artificial neural network or Kalman filter-based inverse kinematics analysis methods have been proposed to minimize the tracking errors of inverse kinematics. However, generating the trajectory of end effectors based on such methods still contain tracking errors, making precise trajectory tracking difficult. To solve this issue, therefore, this study proposes the end effector path control algorithm using artificial neural network and Kalman filter. Furthermore, it demonstrates, through simulation results, that the proposed algorithm can track the trajectory effectively.
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