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Improved Hybrid Trajectory Tracking Algorithm for a 3-link Manipulator Using Artificial Neural Network and Kalman Filter

Dawon Joo and Kiwon Yeom
Department of Human Intelligence and Robot Engineering, Sangmyung University, South Korea

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.

Index Terms—inverse kinematics, artificial neural network, Kalman filter, robot manipulator

Cite: Dawon Joo and Kiwon Yeom, "Improved Hybrid Trajectory Tracking Algorithm for a 3-link Manipulator Using Artificial Neural Network and Kalman Filter," International Journal of Mechanical Engineering and Robotics Research, Vol. 10, No. 2, pp. 60-66, February 2021. DOI: 10.18178/ijmerr.10.2.60-66

​Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.