Evidence map›Paper›PMID 35582266›Full record

ArticleIEEE robotics and automation letters2022

Design and Control of a Highly Redundant Rigid-flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling.

Yingbai Hu, Jian Li, Yongquan Chen, Qiwen Wang, Chuliang Chi, Heng Zhang, Qing Gao, Yuanmin Lan, Zheng Li, Zonggao Mu and 2 more

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  10. Recent Developments of Actuation Mechanisms for Continuum Robots: A Review.International journal of control, automation, and systems · 2023
    Article
  11. Article
  12. A collaborative robot for COVID-19 oropharyngeal swabbing.Robotics and autonomous systems · 2022
    Article
  13. Article
  14. Article
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

12 authors.

Yingbai HuShenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China.ORCID https://orcid.org/0000-0003-2452-3570
Jian LiInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.
Yongquan ChenInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.ORCID https://orcid.org/0000-0003-2493-6033
Qiwen WangInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.
Chuliang ChiInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.
Heng ZhangInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.ORCID https://orcid.org/0000-0003-3673-3751
Qing GaoInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.ORCID https://orcid.org/0000-0002-5395-6175
Yuanmin LanShenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China.
Zheng LiShenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China.ORCID https://orcid.org/0000-0003-4455-0808
Zonggao MuShenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China.ORCID https://orcid.org/0000-0001-9289-5902
Zhenglong SunInstitute of Robotics and Intelligent Manufacturing & School of Science and EngineeringThe Chinese University of Hong Kong Shenzhen 518172 China.ORCID https://orcid.org/0000-0002-8135-1659
Alois KnollChair of Robotics, Artificial Intelligence and Real-time SystemsTechnische Universität München 85748 München Germany.ORCID https://orcid.org/0000-0003-4840-076X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

Indexed as

Deep learning for visual perceptionmedical robots and systemsredundant robotstask and motion planning

Identifiers

PMID35582266
PMCPMC8905611

What OpenQuestion holds

Textmetadata
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.