Evidence map›Paper›PMID 42187414›Full record

ArticleBiomimetics (Basel, Switzerland)2026

Object Recognition-Based Grasping with a Soft Modular Gripper.

Yu Zhang, Fengwen Zhang, Zhihui Guo, Lingkai Luan, Dongbao Sui, Tianshuo Wang, Jiangyu Zhou, Fuyue Zhang, Chen Chen, Dongjie Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomimetics (Basel, Switzerland), 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

11 authors.

Yu ZhangHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Fengwen ZhangHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Zhihui GuoHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Lingkai LuanHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Dongbao SuiJi Hua Laboratory, Foshan 528200, China.ORCID 0000-0002-8801-6415
Tianshuo WangJi Hua Laboratory, Foshan 528200, China.
Jiangyu ZhouHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Fuyue ZhangHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Chen ChenHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Dongjie LiHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.ORCID 0000-0002-1005-7890
Bo YouHeilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.

Funding

Heilongjiang Provincial Natural Science Foundation of China QC2025E035Heilongjiang Provincial Technology Innovation Base Award Program JD25A024Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education MECOF202502State Key Laboratory of Robotics and Systems SKLRS-2025-KF-13State Key Laboratory of Robotics and Systems SKLRS-2026-KF-13
6 · The paper itself

Abstract

Soft modular grippers play a significant role in multiple fields due to their excellent adaptability and flexibility. This paper proposes a modular soft modular gripper driven by pneumatically actuated multi-chambers. The designed soft modular gripper features three operational modes, with its modular fingers employing independently controlled dual chambers. The distal and proximal dual-chamber structure enhances the fingertip force of the modular fingers. Based on classical laminated plate theory and incorporating the large deformation characteristics of soft materials, a relationship between the bending centerline of the fingers and the driving pressure is established, providing a theoretical foundation for grasping tasks executed by the soft modular gripper. The Denavit-Hartenberg (D-H) parameter method is utilized to develop the coordinate system of the soft modular gripper, thereby defining its operational workspace. Visual sensing technology is introduced, incorporating improvements to the YOLOv8-based object recognition and localization framework, which enhances recognition accuracy for target objects and ensures grasping stability.

Indexed as

deformation modelingobject recognitionsoft modular gripperYOLOv8

Identifiers

PMID42187414
PMCPMC13204502

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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.