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Mingsheng Zhong

dblp:377/0068 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%
Computer graphics and multimedia
1 paper
Computational fabrication · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › grasping
adaptive grasping
0.912025
Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025
Robotics › Robot manipulation
grasping
0.912025
Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025
Robotics › Robot manipulation › grasping
soft gripper
0.912025
Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025
Computational fabrication
additive manufacturing
0.312025
Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025

Methods — techniques the papers use, named apart from their topics

static modeling · 1.7kinematic modeling · 1.7finite element simulation · 1.7
YearPublicationVenuePosition
2025 Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping
abstract
Soft grippers have shown promising performance in safe and adaptive grasping tasks. However, they often suffer from limitations in grasping force. To address this challenge, this paper presents a novel pneumatic three-finger soft gripper to achieve robust adaptive grasping. The gripper consists of three identical fingers, each containing a pneumatic bending soft actuator and a pneumatic lateral soft actuator. The bending actuator features a tilted pneumatic network structure, which provides superior bending performance compared to traditional vertical pneumatic network structure. The lateral actuator is equipped with three deflection chambers at the finger root to mimic the lateral motions of a human finger. Kinematic and static models are established to predict the bending angle and grasping force of the soft finger under pressurized air. The performance of the proposed soft finger is analyzed through finite element simulations, and the effect of the chamber tilt angle is also examined. The theoretical and simulation results are compared to verify the validity of the analytical models. Finally, the proposed soft gripper is fabricated by 3D printing and molding. Experimental results show that the gripper is capable of grasping various objects of different sizes, shapes, materials, and weights, and can perform dexterous manipulation tasks, such as cap unscrewing. The proposed soft gripper exhibits significant potential for applications in robotic robust grasping tasks.
Xiantao Sun, Mingsheng Zhong, Zhouzheng Tang, Weihai Chen
ICRA2