Empowering Particle Jamming Soft Gripper with Tactility via Stretchable Optoelectronic Sensing Skin

Author:

Mo Liyan1ORCID,Xie Wenhao1,Qu Jingting1,Xia Jiutian1,Li Yunquan1ORCID,Zhang Yuanfang1,Ren Tao2,Yang Yang3,Yi Juan4,Wu Changchun5,Chen Yonghua5

Affiliation:

1. Shien‐Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou 511442 China

2. School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu 610059 China

3. School of Automation Nanjing University of Information Science and Technology Nanjing 210044 China

4. Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China

5. Department of Mechanical Engineering The University of Hong Kong Hong Kong SAR Hong Kong 999077 China

Abstract

Particle‐jamming soft grippers demonstrate notable shape adaptability and adjustable stiffness, which improve their grasping efficiency. However, integrating tactile sensing into these grippers presents challenges due to the specific properties of the particle jamming mechanism. This study introduces a parallel particle jamming soft gripper equipped with tactile sensing capabilities. The gripper consists of two tactile sensing particle jamming pads (TSPJPs) that are integrated with flexible optoelectronic skins. These skins are made of silicone rubber membranes and are embedded with a 3 × 3 array of stretchable optical waveguide arrays (SOWAs). Testing indicates that incorporating these sensors enhances the gripper's tactile sensing capabilities, with minimal impact on its particle jamming‐based grasping function. A single TSPJP can accurately detect various contact points and estimate the contract forces. The proposed soft gripper can reliably grasp a wide range of objects, varying in shape, hardness, and weight, and it provides detailed tactile feedback on contact locations and the intensity of the grasping through the SOWA sensor. It can precisely distinguish between different grasping postures using a light gradient boosting machine (LightGBM) learning model. Furthermore, it can effectively detect the slippage of grasped objects, facilitating accurate closed‐loop control for secure manipulation.

Publisher

Wiley

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