Evidence map›Paper›PMID 40442069›Full record

ArticleMicrosystems & nanoengineering2025

Onboard visual micro-servoing on robotic surgery tools.

Xu Chen, Michail E Kiziroglou, Eric M Yeatman

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. 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

3 authors.

Xu ChenDepartment of Electrical and Electronic Engineering, Imperial College London, London, UK. xu.chen18@imperial.ac.uk.ORCID http://orcid.org/0000-0002-2586-4556
Michail E KiziroglouDepartment of Electrical and Electronic Engineering, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-0165-2508
Eric M YeatmanDepartment of Electrical and Electronic Engineering, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-0487-2693

Funding

RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/P012779
6 · The paper itself

Abstract

Precision motion actuation is a key technology for miniature medical robotics in a variety of applications, such as optical fibre-based diagnosis and intervention tools. Conventional inductive actuation mechanisms are challenging to scale down. Piezoelectric materials offer a scalable, precise, fast and high-force method but at a limited displacement range. In previous work, the combination of piezoelectric beams (benders) with compliant motion translation structures has been shown to be promising for robotic micro-actuation. In this paper, this approach is employed to implement a three degrees of freedom delta robot, suitable for catheter, diagnostic optical fibre and microsurgery tool manipulation. The fabrication process combines additive manufacturing, origami structuring and piezoelectric beam assembly. Closed-loop control is implemented using a new, on-board visual feedback concept. In contrast to typical optical motion systems, the fully internal visual feedback offers system compactness with precise and reliable camera-to-marker geometry definition. By employment of this method, a delta robot with motion accuracy of 7.5 μm, resolution of 10 μm and 8.1 μm precision is demonstrated. The robot is shown to follow a range of programmable trajectories under these specifications, and to compensate for externally applied forces typically expected during microsurgery manipulations. This is the first, to our knowledge, demonstration of micromotion control using internal visual feedback, and it opens up the way for high-resolution compact microrobots.

Identifiers

PMID40442069
PMCPMC12122923

What OpenQuestion holds

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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.