Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137858
Title: Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning
Authors: Li, Xiaoguo
Cao, Lin
Tiong, Anthony Meng Huat
Phan, Phuoc Thien
Phee, Soo Jay
Keywords: Engineering::Mechanical engineering::Robots
Issue Date: 2019
Source: 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
Journal: Mechanism and Machine Theory
Abstract: 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.
URI: https://hdl.handle.net/10356/137858
ISSN: 0094-114X
DOI: 10.1016/j.mechmachtheory.2018.12.035
Rights: © 2019 Elsevier Ltd. All rights reserved. This paper was published in Mechanism and Machine Theory and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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