Evidence map›Paper›PMID 37504159›Full record

ArticleBiomimetics (Basel, Switzerland)2023

Design and Motion Analysis of a Soft-Limb Robot Inspired by Bacterial Flagella.

Changlong Ye, Zhanpeng Liu, Suyang Yu, Zifu Fan, Yinchao Wang

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2023. 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

5 authors.

Changlong YeSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Zhanpeng LiuSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Suyang YuSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Zifu FanSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 569830, Singapore.
Yinchao WangSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.

Funding

Liaoning Provincial Nature Foundation 20180520033National Natural Science Foundation of China 52005349The Liaoning Provincial Education Department Fund-"Seedling Raising" Project for Young Scien-tific and Technological Talents JYT2020136
6 · The paper itself

Abstract

Soft robots demonstrate an impressive ability to adapt to objects and environments. However, current soft mobile robots often use a single mode of movement. This gives soft robots good locomotion performance in specific environments but poor performance in others. In this paper, we propose a leg-wheel mechanism inspired by bacterial flagella and use it to design a leg-wheel robot. This mechanism employs a tendon-driven continuum structure to replicate the bacterial flagellar filaments, while servo and gear components mimic the action of bacterial flagellar motors. By utilizing twisting and swinging motions of the continuum structure, the robot achieves both wheeled and legged locomotion. The paper provides comprehensive descriptions and detailed kinematic analysis of the mechanism and the robot. To verify the feasibility of the robot, a prototype was implemented, and experiments were performed on legged mode, wheeled mode, and post-overturning motion. The experimental results demonstrate that the robot can achieve legged and wheeled motions. Moreover, it is also demonstrated that the robot still has mobility after overturning. This expands the applicability scenarios of the current soft mobile robot.

Indexed as

bioinspired robotgait planningleg–wheel mechanismquadruped robottendon drive

Identifiers

PMID37504159
PMCPMC10377722

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.