Evidence map›Paper›PMID 34833614›Full record

ArticleSensors (Basel, Switzerland)2021

A Modular Cooperative Wall-Climbing Robot Based on Internal Soft Bone.

Wenkai Huang, Wei Hu, Tao Zou, Junlong Xiao, Puwei Lu, Hongquan Li

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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
0.3field-weighted citation impact, top 49% of its field
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, 4 citations in OpenAlex.

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

6 authors at 1 institution in 1 country.

Wenkai HuangSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0003-3111-7511
Wei HuSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
Tao ZouSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
Junlong XiaoSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0003-3854-8760
Puwei LuSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0002-0492-0782
Hongquan LiSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
Guangzhou University · CN

Funding

This research was funded by the Guangzhou Science and Technology Planning Project 202002030279
6 · The paper itself

Abstract

Most existing wall-climbing robots have a fixed range of load capacity and a step distance that is small and mostly immutable. It is therefore difficult for them to adapt to a discontinuous wall with particularly large gaps. Based on a modular design and inspired by leech peristalsis and internal soft-bone connection, a bionic crawling modular wall-climbing robot is proposed in this paper. The robot demonstrates the ability to handle variable load characteristics by carrying different numbers of modules. Multiple motion modules are coupled with the internal soft bone so that they work together, giving the robot variable-step-distance functionality. This paper establishes the robotic kinematics model, presents the finite element simulation analysis of the model, and introduces the design of the multi-module cooperative-motion method. Our experiments show that the advantage of variable step distance allows the robot not only to quickly climb and turn on walls, but also to cross discontinuous walls. The maximum climbing step distance of the robot can reach 3.6 times the length of the module and can span a discontinuous wall with a space of 150 mm; the load capacity increases with the number of modules in series. The maximum load that N modules can carry is about 1.3 times the self-weight.

Indexed as

RoboticsBiomechanical PhenomenaBionicsEquipment DesignMotioninternal soft bonemodularpayload power factorvariable loadvariable step distancewall-climbing robot

Identifiers

PMID34833614
PMCPMC8622837
OpenAlexW3211877272

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.