Evidence map›Paper›PMID 39727776›Full record

ArticleBiomimetics (Basel, Switzerland)2024

Neural Network-Based Shape Analysis and Control of Continuum Objects.

Yuqiao Dai, Shilin Zhang, Wei Cheng, Peng Li

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Yuqiao DaiSchool of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
Shilin ZhangSchool of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
Wei ChengDevol Advanced Automation, Inc., Jiaan Science Park, Liuxian 1st Road, Bao an District, Shenzhen 518101, China.
Peng LiSchool of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.

Funding

Natural Science Foundation of China 62073099Shenzhen Science and Technology Program JSGG20211108092806010
6 · The paper itself

Abstract

Soft robots are gaining increasing attention in current robotics research due to their continuum structure. However, accurately recognizing and reproducing the shape of such continuum robots remains a challenge. In this paper, we propose a novel approach that combines contour extraction with camera reconstruction to obtain shape features. Neural networks are employed to model the relationship between motor inputs and the resulting shape output. A simulation environment is established to verify the shape estimation and shape control of the flexible continuum. The outcomes demonstrate that this approach effectively predicts and reproduces the shape of flexible continuum robots, providing a promising solution for continuum shape control.

Indexed as

continuum objectneural networksshape analysisshape control

Identifiers

PMID39727776
PMCPMC11673849

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.