Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152730
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dc.contributor.authorLi, Xiaoguoen_US
dc.date.accessioned2021-09-20T08:21:07Z-
dc.date.available2021-09-20T08:21:07Z-
dc.date.issued2021-
dc.identifier.citationLi, X. (2021). Haptic feedback for flexible endoscopic surgical robot using data-driven methods. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152730en_US
dc.identifier.urihttps://hdl.handle.net/10356/152730-
dc.description.abstractRobot-assisted Natural Orifice Transluminal Endoscopic Surgery (NOTES) has been an emerging field of application in recent years and has demonstrated great potential and reliability in performing operations inside the peritoneal cavity while avoiding the necessity of abdominal incisions. It offers benefits such as enhanced operation precision, minimized tissue damage, and easier recovery for the patients. To actuate the joints of end-effectors through narrow and tortuous paths, tendon-sheath mechanism (TSM) is widely adopted for flexible endoscopic robotic systems. However, the friction of TSM introduces nonlinearity and backlash hysteresis which degrades the control precision and creates hurdles for developing haptic feedback. It is difficult to mount force sensors on small end-effectors due to space limitation, wiring, and sterilization issues. Previous techniques for modeling the tendon-sheath system force transmission are associated with problems such as discontinuity when the system operates at the vicinity of zero velocity and complex ad-hoc parameter identification process. This study proposes a deep learning approach to predicting the distal force of TSMs based on proximal-end measurements. A TSM-driven robotic system manipulating biological tissue was developed to collect training and testing data for deep learning. A two-stage data-driven method was developed to make dynamic distal-end force prediction of a flexible endoscopic robot without prior knowledge of its configuration. In stage one, a convolutional neural network is used to estimate the sheath cumulative bending angle based on the proximal-end force responses of the robot to a probing signal; in stage two, a combination of two long-short-term-memory models pre-trained for the bending angles nearest to the estimated angle (obtained in stage one) makes dynamic estimations of the distal-end force of the robot. The proposed approach overcomes the challenges due to unknown TSM configurations and can robustly identify the correct force hysteresis phases of TSMs. The force prediction is continuous, robust, and has a mean RMSE of 0.1711 N. This method was validated on an actual flexible surgical robot.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationNRFI2016-07en_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Mechanical engineering::Robotsen_US
dc.titleHaptic feedback for flexible endoscopic surgical robot using data-driven methodsen_US
dc.typeThesis-Doctor of Philosophyen_US
dc.contributor.supervisorPhee Soo Jay, Louisen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeDoctor of Philosophyen_US
dc.contributor.researchRobotics Research Centreen_US
dc.identifier.doi10.32657/10356/152730-
dc.contributor.supervisoremailMSJPhee@ntu.edu.sgen_US
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