TY - GEN
T1 - Improving control precision and motion adaptiveness for surgical robot with recurrent neural network
AU - Li, Yangming
AU - Li, Shuai
AU - Caballero, David
AU - Miyasaka, Muneaki
AU - Lewis, Andrew
AU - Hannaford, Blake
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Surgical robot research is driven by the desire of improving surgical outcomes. This paper proposed a Recurrent Neural Network based controller to address two problems: 1) improving control precision, 2) increasing adaptiveness for robot motion (explained in Section I). RNN was adopted in this work mainly because 1) the problem formulation naturally matches RNN structure, 2) RNN has advantages as an biologically inspired method. The proposed method was explained in detail and analysis shows that the proposed method is able to dynamically regulate outputs to increase the adaptiveness and the control precision. This paper uses Raven II surgical robot as an example to show the application of the proposed method, and the numeral simulation results from the proposed method and three other controllers show that the proposed method has improved precision, improved high robustness against noise and increased movement smoothness, and it keeps the manipulator links as far away as possible from physical boundaries, which potentially increases surgical safety and leads to improved surgical outcomes.
AB - Surgical robot research is driven by the desire of improving surgical outcomes. This paper proposed a Recurrent Neural Network based controller to address two problems: 1) improving control precision, 2) increasing adaptiveness for robot motion (explained in Section I). RNN was adopted in this work mainly because 1) the problem formulation naturally matches RNN structure, 2) RNN has advantages as an biologically inspired method. The proposed method was explained in detail and analysis shows that the proposed method is able to dynamically regulate outputs to increase the adaptiveness and the control precision. This paper uses Raven II surgical robot as an example to show the application of the proposed method, and the numeral simulation results from the proposed method and three other controllers show that the proposed method has improved precision, improved high robustness against noise and increased movement smoothness, and it keeps the manipulator links as far away as possible from physical boundaries, which potentially increases surgical safety and leads to improved surgical outcomes.
KW - Bioinspired Controller
KW - Kinematic Control
KW - Recurrent Neural Network
KW - Surgical Robot
KW - Surgical Safety
UR - http://www.scopus.com/inward/record.url?scp=85041951069&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206197
DO - 10.1109/IROS.2017.8206197
M3 - Conference article published in proceeding or book
AN - SCOPUS:85041951069
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3538
EP - 3543
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -