Natural Entanglement Inspired Cilia‐Like Soft Gripper for Rapid Adaptive Grasping

Self‐adaptive, easy‐to‐control, and low‐cost gripper devices are indispensable in manufacturing and agriculture. However, the existing soft grippers cannot provide high response speed and firm grasping. Inspired by the natural, active entanglement behaviors of animals and plants, a rapid, cilia‐like soft gripper design is proposed for grasping various objects via envelopes formed by the self‐entanglement of multiple hollowed silicone tubes. The basic entanglement unit comprises a hollow, soft silicone tube with an actuation wire inside, which leads to compression and entanglement by fixing the front of the tube and drawing the actuation wire. Using multiple entanglement units enables sufficient mechanical interlocking between deformed tubes and grasped objects, avoiding the reliance on contact force control. Experimental results demonstrate that the developed soft gripper, with a cost lower than one dollar, can complete adaptive grasping within 1 s. The grasping success rate can reach 100% in grasping common irregular‐shaped daily objects within the effective grasping range of the entanglement units. The design paves the way for harnessing the potential of embodied intelligence in soft robots, enabling fast and universal grasping.

Authors

Institutions

Publication Details

Journal
Advanced Intelligent Systems
Published
2025-10-13
DOI
https://doi.org/10.1002/aisy.202500468
Citations
1
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.71

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Natural Entanglement Inspired Cilia‐Like Soft Gripper for Rapid Adaptive Grasping

Yukang Yan, Zichen Xu, Yuanhe Chen, Qingsong Xu et al.
1 citations
Advanced Intelligent Systems
Modular Robots and Swarm Intelligence
0.71
article

Natural Entanglement Inspired Cilia‐Like Soft Gripper for Rapid Adaptive Grasping

Yukang Yan, Zichen Xu, Yuanhe Chen, Qingsong Xu, Xianli Wang
article en
1 citations

Abstract

Self‐adaptive, easy‐to‐control, and low‐cost gripper devices are indispensable in manufacturing and agriculture. However, the existing soft grippers cannot provide high response speed and firm grasping. Inspired by the natural, active entanglement behaviors of animals and plants, a rapid, cilia‐like soft gripper design is proposed for grasping various objects via envelopes formed by the self‐entanglement of multiple hollowed silicone tubes. The basic entanglement unit comprises a hollow, soft silicone tube with an actuation wire inside, which leads to compression and entanglement by fixing the front of the tube and drawing the actuation wire. Using multiple entanglement units enables sufficient mechanical interlocking between deformed tubes and grasped objects, avoiding the reliance on contact force control. Experimental results demonstrate that the developed soft gripper, with a cost lower than one dollar, can complete adaptive grasping within 1 s. The grasping success rate can reach 100% in grasping common irregular‐shaped daily objects within the effective grasping range of the entanglement units. The design paves the way for harnessing the potential of embodied intelligence in soft robots, enabling fast and universal grasping.

Advanced Intelligent SystemsVol. 8(1)
University of Macau (MO)
United Mitochondrial Disease Foundation, National Natural Science Foundation of China, Universidade de Macau, Fundo para o Desenvolvimento das Ciências e da Tecnologia
Openalex Percentile: Top 26%
Modular Robots and Swarm Intelligence
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.