Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning
Accurate haptic feedback is highly challenging for flexible endoscopic surgical robots due to space limitation for sensors on small end-effectors and critical force hysteresis of their tendon-sheath mechanisms (TSMs). This paper proposes a deep learning approach to predicting the distal force of TSM...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/137858 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-137858 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1378582023-03-04T17:21:04Z Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning Li, Xiaoguo Cao, Lin Tiong, Anthony Meng Huat Phan, Phuoc Thien Phee, Soo Jay School of Mechanical and Aerospace Engineering Robotics Research Centre Engineering::Mechanical engineering::Robots Haptic Feedback Surgical Robot Accurate haptic feedback is highly challenging for flexible endoscopic surgical robots due to space limitation for sensors on small end-effectors and critical force hysteresis of their tendon-sheath mechanisms (TSMs). This paper proposes a deep learning approach to predicting the distal force of TSMs when manipulating a biological tissue based on only proximal-end measurements. Both Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) were investigated to study their capabilities of making sequential distal force predictions. The results were compared with those of the conventional modelling approach. It was observed that, when sufficient data was provided for training, RNN achieved the most accurate prediction (RMSE = 0.0219 N) in experiments with constant system velocity. The effects of insufficient training data, varying system velocity and irregular motion trajectories on the performance of RNN were further studied. Notably, RNN could precisely identify the current system phase in the force hysteresis profile and can be applied to TSMs with realistic non-periodic movement such as manual manipulation trajectory (RSME = 0.2287 N). The proposed approach can be applied to any TSM-driven robotic systems for accurate haptic feedback without requiring sensors at the distal ends of the robots. Accepted version 2020-04-16T08:43:52Z 2020-04-16T08:43:52Z 2019 Journal Article Li, X., Cao, L., Tiong, A. M. H., Phan, P. T., & Phee, S. J. (2019). Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning. Mechanism and Machine Theory, 134, 323-337. doi:10.1016/j.mechmachtheory.2018.12.035 0094-114X https://hdl.handle.net/10356/137858 10.1016/j.mechmachtheory.2018.12.035 2-s2.0-85059597558 134 323 337 en Mechanism and Machine Theory © 2019 Elsevier Ltd. All rights reserved. This paper was published in Mechanism and Machine Theory and is made available with permission of Elsevier Ltd. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Mechanical engineering::Robots Haptic Feedback Surgical Robot |
spellingShingle |
Engineering::Mechanical engineering::Robots Haptic Feedback Surgical Robot Li, Xiaoguo Cao, Lin Tiong, Anthony Meng Huat Phan, Phuoc Thien Phee, Soo Jay Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
description |
Accurate haptic feedback is highly challenging for flexible endoscopic surgical robots due to space limitation for sensors on small end-effectors and critical force hysteresis of their tendon-sheath mechanisms (TSMs). This paper proposes a deep learning approach to predicting the distal force of TSMs when manipulating a biological tissue based on only proximal-end measurements. Both Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) were investigated to study their capabilities of making sequential distal force predictions. The results were compared with those of the conventional modelling approach. It was observed that, when sufficient data was provided for training, RNN achieved the most accurate prediction (RMSE = 0.0219 N) in experiments with constant system velocity. The effects of insufficient training data, varying system velocity and irregular motion trajectories on the performance of RNN were further studied. Notably, RNN could precisely identify the current system phase in the force hysteresis profile and can be applied to TSMs with realistic non-periodic movement such as manual manipulation trajectory (RSME = 0.2287 N). The proposed approach can be applied to any TSM-driven robotic systems for accurate haptic feedback without requiring sensors at the distal ends of the robots. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Li, Xiaoguo Cao, Lin Tiong, Anthony Meng Huat Phan, Phuoc Thien Phee, Soo Jay |
format |
Article |
author |
Li, Xiaoguo Cao, Lin Tiong, Anthony Meng Huat Phan, Phuoc Thien Phee, Soo Jay |
author_sort |
Li, Xiaoguo |
title |
Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
title_short |
Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
title_full |
Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
title_fullStr |
Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
title_full_unstemmed |
Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
title_sort |
distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/137858 |
_version_ |
1759857764702617600 |