Evidence map›Paper›PMID 42873783›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Enhanced Acoustic Adhesion Enabling Agile and Small Wall-Climbing Robot Design With Anisotropic Origami Structure.

Xinxian Chen, Kanglong Yuan, Ao Qin, Yikun Liu, Yusen Ma, Jun Peng

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. 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

6 authors.

Xinxian Chen *School of Mechanical Engineering, Tsinghua University, Beijing, China.
Kanglong Yuan *School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0009-0006-6945-3261
Ao QinSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Yikun LiuSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0009-0001-4745-040X
Yusen MaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Jun PengSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0000-0002-2337-5273

Funding

Key R&D Program of Shaanxi, China 2023-YBGY-358National Natural Science Foundation of China 52175248
6 · The paper itself

Abstract

Small inspection robots are highly desirable for detecting damage within confined spaces. By vibrating a disk on a surface, a thin air layer with negative pressure-known as acoustic adhesion-is generated, which offers a strategy for creating compact yet strongly climbing robots. However, it is highly sensitive to disturbances. Any transmitted vibrations or improper torque can compromise the robot's airtightness, causing it to fall. This fatal flaw has hindered the development of reliable acoustic climbing robots. Here, we present a 28 g, 50 mm inspection robot capable of navigating vertical walls (8 cm/s) and ceilings (20 cm/s) while carrying a 100 g payload. We decouple vibration and load effects using a ring connection, approximately doubling the adhesion. Moreover, an anisotropic origami spring is designed to maintain close contact between the disk and the substrate, ensuring durable, robust adhesion. By combining acoustic adhesion with optimized structural designs, we overcome two major challenges: failure from vibration coupling and demanding assembly. Our robot thereby achieves high mobility and a large payload capacity. We demonstrate the robot's agility and intelligence through maze navigation and inspection of real blades, in which it autonomously adjusts its path. This work offers a potential solution for in-situ damage detection.

Indexed as

acoustic adhesioninspection robotorigami springvisual identificationwall‐climbing robot

Identifiers

PMID42873783
PMCPMC13667888

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.