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January 18, 2026Advanced Intelligent Systems0 citationsOpen Access

A Multi‐instrument Recognition and Autonomous Tracking Control Method for Robot‐Assisted Endoscopic Adjustment

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ZYZijie YangShenzhen UniversityJHJunyi HuKunming University of Science and TechnologyYHYu HanShenzhen University

Key Points

  • The aim is to create a robot-assisted system for accurate positioning of surgical instruments and endoscope control.
  • Developed a lightweight detection model for surgical instruments without markers.
  • Introduced an attention mechanism for improved detection accuracy in complex environments.
  • Proposed a hierarchical multiconstraint controller that manages various surgical constraints.
  • Designed upper computer software and conducted numerical simulations alongside actual tracking experiments.
  • Average tracking distance reduced by 35.86% in simulation and 13.90% in actual experiments.
  • Compared favorably against pseudo-inverse and adaptive control methods.
  • Effectively controlled constraints on joint limits and endoscope depth.

Abstract

This study constructs a robot‐assisted endoscopic adjustment system, which can achieve real‐time precise positioning of surgical instruments (SI) and visual servoing control (VSC) of EFOV under multiple constraints. On the one hand, based on the problem of SI recognition without markers, this study develops a lightweight SI detection model, which improves the detection accuracy of SI in complex surgical environments by introducing an attention mechanism. On the other hand, a hierarchical multiconstraint controller based on image feature points and constraint items is proposed. While achieving VSC, it also realizes constraints on the manipulator joint limits, the endoscope insertion depth, and the remote center of motion. Ultimately, an upper computer software interface is designed, as well as a numerical simulation and an actual SI tracking experiment are carried out. By conducting experiments in these two categories and comparing them with pseudo‐inverse and adaptive control methods, it can be concluded that the average tracking distance are reduced by 35.86% and 28.47% in simulation as well as 13.90% and 11.04% in actual SI tracking experiments, respectively, and constraint items can be effectively controlled and corrected. The results show that this method can adjust the surgical EFOV stably, safely and quickly.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/696c7877eb60fb80d13969d8https://doi.org/10.1002/aisy.202500938
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