Gecko Toe Pad-Inspired Robotic Gripper with Rapidly and Precisely Tunable Adhesion

Gecko-inspired dry adhesives have shown great potential in the field of robotics. However, there is still a large gap between current artificial adhesive-based grippers and natural geckos, especially in terms of precise and fast control of adhesion, which is an important capability for robotic gripper systems, since the targets to be gripped may vary in size and weight (including thin, fragile, soft, and deformable), and manipulation must be fast to meet high productivity requirements. Here, we propose a robotic gripper that is able to switch adhesion rapidly (in less than 0.5 s) to grasp and release objects of various sizes and weights (such as glass substrates, fragile silicon wafers, and deformable polyethylene terephthalate films) by mimicking the self-peeling behavior of gecko toe pads. The gripper retains the fast and stable manipulation of the conventional mechanical gripper, which is more reliable and has a higher load capacity than stimulus-responsive switchable adhesives. Systematic experimental and theoretical studies provide insights into the construction and analysis of the self-peeling model and mechanism to identify certain crucial parameters affecting the self-peeling behavior. Furthermore, a strategy for active adhesion control (i.e., precise adhesion modulation) is integrated by introducing a preset peeling angle <i>θ<sub>B</sub></i> , providing the gripper with a quantitative criterion for adjusting the adhesion strength (0 to 82.77 kPa) according to the requirements of practical applications. The gripper has great potential to be an alternative end-operating gripper for robotic systems, opening an avenue for the development of robotic manipulation.

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Publication Details

Journal
Research
Published
2025-01-01
DOI
https://doi.org/10.34133/research.0687
Citations
4
Primary Topic
Adhesion, Friction, and Surface Interactions
Type
article
Field-Weighted Citation Impact
3.45
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article

Gecko Toe Pad-Inspired Robotic Gripper with Rapidly and Precisely Tunable Adhesion

Xiangmeng Li, Xijing Zhu, Shuai Li, Mingxin Liu et al.
4 citations
Research
Adhesion, Friction, and Surface Interactions
3.45
article

Gecko Toe Pad-Inspired Robotic Gripper with Rapidly and Precisely Tunable Adhesion

Xiangmeng Li, Xijing Zhu, Shuai Li, Mingxin Liu, Jinyou Shao, Hongmiao Tian
article en
4 citations

Abstract

Gecko-inspired dry adhesives have shown great potential in the field of robotics. However, there is still a large gap between current artificial adhesive-based grippers and natural geckos, especially in terms of precise and fast control of adhesion, which is an important capability for robotic gripper systems, since the targets to be gripped may vary in size and weight (including thin, fragile, soft, and deformable), and manipulation must be fast to meet high productivity requirements. Here, we propose a robotic gripper that is able to switch adhesion rapidly (in less than 0.5 s) to grasp and release objects of various sizes and weights (such as glass substrates, fragile silicon wafers, and deformable polyethylene terephthalate films) by mimicking the self-peeling behavior of gecko toe pads. The gripper retains the fast and stable manipulation of the conventional mechanical gripper, which is more reliable and has a higher load capacity than stimulus-responsive switchable adhesives. Systematic experimental and theoretical studies provide insights into the construction and analysis of the self-peeling model and mechanism to identify certain crucial parameters affecting the self-peeling behavior. Furthermore, a strategy for active adhesion control (i.e., precise adhesion modulation) is integrated by introducing a preset peeling angle <i>θ<sub>B</sub></i> , providing the gripper with a quantitative criterion for adjusting the adhesion strength (0 to 82.77 kPa) according to the requirements of practical applications. The gripper has great potential to be an alternative end-operating gripper for robotic systems, opening an avenue for the development of robotic manipulation.

ResearchVol. 8
North University of China (CN), Shanxi University (CN), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 8%
Adhesion, Friction, and Surface Interactions
3.45
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