mlsanityData to 7 Oct 2026

SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm

arXiv 2406.18388 ↗First posted 26 Jun 2024cs.RO cs.AIcited by 2

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Abstract (arXiv)

Cable-Driven Continuum Manipulators (CDCMs) enable scar-free procedures but face limitations in workspace and control accuracy due to hysteresis. We introduce an extensible CDCM with a Semi-active Mechanism (SAM) and develop a real-time hysteresis compensation control algorithm using a Temporal Convolutional Network (TCN) based on data collected from fiducial markers and RGBD sensing. Performance validation shows the proposed controller significantly reduces hysteresis by up to 69.5% in random trajectory tracking test and approximately 26% in the box pointing task. The SAM mechanism enables access to various lesions without damaging surrounding tissues. The proposed controller with TCN-based compensation effectively predicts hysteresis behavior and minimizes position and joint angle errors in real-time, which has the potential to enhance surgical task performance.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 47 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

6 of 47 references.

References · 6

  1. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  2. 2020Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humanscited by 7
  3. 2021Shape-adaptive Hysteresis Compensation for Tendon-driven Continuum Manipulatorscited by 4
  4. 2020Non-linear Hysteresis Compensation of a Tendon-sheath-driven Robotic Manipulator using Motor Currentcited by 3
  5. 2024Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Networkcited by 3
  6. 2024Optimizing Base Placement of Surgical Robot: Kinematics Data-Driven Approach by Analyzing Working Patterncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.