Inverse Kinematics for Robot Arm Using Minimum Mean Square Error
Changeui Shin, Junho Park, Woong Jeong, Jaewook Lee, YoungJun Joo, HoSeong Kwak
Abstract
This paper considers the inverse kinematics problem of a robotic arm applying minimum mean square error with variance-based control. The proposed algorithm achieves optimal results by minimizing the average error, even when considering variance calculations. Its performance is comparable to that of the algorithm that utilizes optimally tuned singular value decomposition (SVD). The calculated variance values are added to the diagonal terms of the matrix as in the damped least squares method in the inverse matrix operation. This indicates that optimal performance can be achieved even when a Moore-Penrose pseudo-inverse matrix is employed instead of SVD. The effectiveness of the proposed method is validated with seven-degree-of-freedom (7-DoF) (1 rail + 6-DoF arm) and 6-DoF robots. By introducing practical error control methods, this paper contributes to enhancing the overall comprehension of error-related algorithms.
BibTeX
@inproceedings{iros2025_inversekinematic,
title = {Inverse Kinematics for Robot Arm Using Minimum Mean Square Error},
author = {Changeui Shin and Junho Park and Woong Jeong and Jaewook Lee and YoungJun Joo and HoSeong Kwak},
booktitle = {IROS 2025},
year = {2025}
}