A Modular 3-Degrees-of-Freedom Force Sensor for Robot-Assisted Minimally Invasive Surgery Research

被引:10
作者
Chua, Zonghe [1 ]
Okamura, Allison M. M. [2 ]
机构
[1] Case Western Reserve Univ, Dept Elect Comp & Syst Engn, 10900 Euclid Ave,Glennan Bldg 514A, Cleveland Hts, OH 44106 USA
[2] Stanford Univ, Dept Mech Engn, Stanford, CA 94305 USA
关键词
force sensing; minimally invasive surgical robotics; medical robotics; SENSING CAPABILITY; DESIGN; SKILL;
D O I
10.3390/s23115230
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Effective force modulation during tissue manipulation is important for ensuring safe, robot-assisted, minimally invasive surgery (RMIS). Strict requirements for in vivo applications have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. Due to this trade-off, there are no commercial, off-the-shelf, 3-degrees-of-freedom (3DoF) force sensors for RMIS available to researchers. This makes it challenging to develop new approaches to indirect sensing and haptic feedback for bimanual telesurgical manipulation. We present a modular 3DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements and by using commercial load cells and common electromechanical fabrication techniques. The sensor has a range of +/- 5N axially and +/- 3N laterally with errors of below 0.15N and maximum errors below 11% of the sensing range in all directions. During telemanipulation, a pair of jaw-mounted sensors achieved average errors below 0.15N in all directions. It achieved an average grip force error of 0.156 N. The sensor is for bimanual haptic feedback and robotic force control in delicate tissue telemanipulation. As an open-source design, the sensors can be adapted to suit other non-RMIS robotic applications.
引用
收藏
页数:17
相关论文
共 44 条
[1]  
Aviles Angelica I., 2014, 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA). Proceedings, P1, DOI 10.1109/IPTA.2014.7001941
[2]   Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach [J].
Aviles, Angelica I. ;
Alsaleh, Samar M. ;
Hahn, James K. ;
Casals, Alicia .
IEEE TRANSACTIONS ON HAPTICS, 2017, 10 (03) :431-443
[3]   Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions [J].
Bahar, Lidor ;
Sharon, Yarden ;
Nisky, Ilana .
FRONTIERS IN NEUROROBOTICS, 2020, 13
[4]   Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer [J].
Brown, Jeremy D. ;
O'Brien, Conor E. ;
Leung, Sarah C. ;
Dumon, Kristoffel R. ;
Lee, David I. ;
Kuchenbecker, Katherine J. .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2017, 64 (09) :2263-2275
[5]   Toward Force Estimation in Robot-Assisted Surgery using Deep Learning with Vision and Robot State [J].
Chua, Zonghe ;
Jarc, Anthony M. ;
Okamura, Allison M. .
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021), 2021, :12335-12341
[6]   Task Dynamics of Prior Training Influence Visual Force Estimation Ability During Teleoperation [J].
Chua, Zonghe ;
Jarc, Anthony M. ;
Wren, Sherry M. ;
Nisky, Ilana ;
Okamura, Allison M. .
IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2020, 2 (04) :586-597
[7]  
Dai Y, 2017, IEEE ENG MED BIO, P3936, DOI 10.1109/EMBC.2017.8037717
[8]   Development of the X-Perce-A Universal FBG-Based Force Sensing Kit for Laparoscopic Surgical Robot [J].
Du, Chengjin ;
Wei, Dehao ;
Wang, Han ;
Wang, Weidong ;
Dong, Jian ;
Huo, Haitao ;
Li, Yingtian .
IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 2022, 4 (01) :183-193
[9]   Haptics in robot-assisted surgery: Challenges and benefits [J].
Enayati N. ;
De Momi E. ;
Ferrigno G. .
IEEE Reviews in Biomedical Engineering, 2016, 9 :49-65
[10]  
Figliola RS, 2020, Theory and design for mechanical measurements, V5th