Evidence map›Paper›PMID 42396094›Full record

ArticleIEEE robotics and automation letters2026

Stable Tracking-in-the-Loop Control of Cable-Driven RCM Surgical Manipulators under Erroneous Kinematic Chains.

Neelay Joglekar, Fei Liu, Florian Richter, Michael C Yip

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Neelay JoglekarNeelay Joglekar is with the Robotics Institute in the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Fei LiuFei Liu is with the Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville, Knoxville, TN 37996, USA.
Florian RichterFlorian Richter and Michael C. Yip are with the Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.
Michael C YipFlorian Richter and Michael C. Yip are with the Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Funding

AI-assisted Annotation of Minimally Invasive Surgical Video for Building Information-Rich Datasets and Data-Efficient Learning SystemsR21EB036284 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LIU, SHANGLEI, YIP, MICHAEL · 2024 to 2024
$394k
NIBIB NIH HHS R21 EB036284
6 · The paper itself

Abstract

Remote Center of Motion (RCM) robotic manipulators have revolutionized Minimally Invasive Surgery, enabling precise, dexterous surgical manipulation within the patient's body cavity without disturbing the insertion point on the patient. Accurate RCM tool control is vital for incorporating autonomous subtasks like suturing, blood suction, and tumor resection into robotic surgical procedures, reducing surgeon fatigue and improving patient outcomes. However, these cable-driven systems are subject to significant joint reading errors, corrupting the kinematics computation necessary to perform control. Although visual tracking with endoscopic cameras can correct errors on in-view joints, errors in the kinematic chain prior to the insertion point are irreparable because they remain out of view. No prior work has characterized the stability of control under these conditions. We fill this gap by designing a provably stable tracking-in-the-loop controller for the out-of-view portion of the RCM manipulator kinematic chain. We additionally incorporate this controller into a bilevel control scheme for the full kinematic chain. We rigorously benchmark our method in simulated and real world settings to verify our theoretical findings. Our work provides key insights into the next steps required for the transition from teleoperated to autonomous surgery.

Indexed as

Computer Vision for Medical RoboticsLaparoscopyMedical Robots and SystemsSurgical Robotics

Identifiers

PMID42396094
PMCPMC13325479

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.