Hierarchical optimization Control of Redundant Manipulator for Robot-assisted Minimally Invasive Surgery
Yingbai Hu, Hang Su, Guang Chen, Giancarlo Ferrigno, Elena De Momi, Alois Knoll
Abstract
For the time varying optimization problem, the tracking error cannot converge to zero at the finite time because of the optimal solution changing over time. This paper proposes a novel varying parameter recurrent neural network (VPRNN) based hierarchical optimization of a 7-DoF surgical manipulator for Robot-Assisted Minimally Invasive Surgery (RAMIS), which guarantees task tracking, Remote Center of Motion (RCM) and manipulability index optimization. A theoretically grounded hierarchical optimization framework based is introduced to control multiple tasks based on their priority. Finally, the effectiveness of the proposed control strategy is demonstrated with both simulation and experimental results. The results show that the proposed VPRNN-based method can optimal three tasks at the same time and have better performance than previous work.
BibTeX
@inproceedings{iros2020_hierarchicalopti,
title = {Hierarchical optimization Control of Redundant Manipulator for Robot-assisted Minimally Invasive Surgery},
author = {Yingbai Hu and Hang Su and Guang Chen and Giancarlo Ferrigno and Elena De Momi and Alois Knoll},
booktitle = {IROS 2020},
year = {2020}
}