2019
DOI: 10.1016/j.mechmachtheory.2018.12.035
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Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning

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Cited by 63 publications
(43 citation statements)
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References 38 publications
(61 reference statements)
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“…This means that the iPPROM risk could be lowered if this instrument was used in a real TTTS procedure. However, the current basic hysteresis modeling approach should be improved by e.g., using hysteresis compensation (Mei et al, 2017;Capace et al, 2019) or deep learning algorithms (Li et al, 2019). This would probably encourage the operator to use the flexible feature of the instrument even more, since it would be more responsive to the user input.…”
Section: Discussionmentioning
confidence: 99%
“…This means that the iPPROM risk could be lowered if this instrument was used in a real TTTS procedure. However, the current basic hysteresis modeling approach should be improved by e.g., using hysteresis compensation (Mei et al, 2017;Capace et al, 2019) or deep learning algorithms (Li et al, 2019). This would probably encourage the operator to use the flexible feature of the instrument even more, since it would be more responsive to the user input.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, the HFAM generates its motion via a local extension of an individual muscle segment, which is analogous to the natural behavior of certain plant cells that are lengthened or shortened when being pressurized [64], enabling uniform distributions of the motion and its generated mechanical force while maintaining its energy regardless of the distance. In contrast, the motion and force output of the tendon-driven mechanism highly depends on the routing path and the distance of its power source located at its proximal end [65,66]. In applications that require highly tortuous paths and multi-loop configurations such as flexible surgical robots or wearable exoskeletons where tendon-sheath or Bowden cable mechanisms are currently dominant, a high force loss is unavoidable [2,67].…”
Section: B Operating Principle Of the Hfam And Modelingmentioning
confidence: 99%
“…The adaptive algorithm is more suitable for the hysteretic modelling of tendon-sheath mechanisms (TSMs). Li et al [8,9] proposed a deep learning method that predicts the far-end force of TSMs based on the near-end measurements and can achieve accurate predictions in experiments with constant system speeds. However, these methods usually assume that the casing is fixed, which is difficult to achieve in the application of actual robotic arms.…”
Section: Introductionmentioning
confidence: 99%