A Bioinspired Gripper with Sequential Motion and Mutable Posture Enabled by Antagonistic Mechanism
Tianhong Wang, Tao Jin, Quan Zhang, Long Li, Guopeng Wang, Yingzhong Tian, Sicheng Yi, Yangqiao Lin
- 发表年份
- 2023
- 引用次数
- 12
- 访问权限
- 开放获取
摘要
Human finger excels at delicate and dynamic gripping tasks via coordinating soft tissues and rigid components, which is yet considerably challenging for robotic grippers. Herein, inspired by the human finger, a soft‐rigid structured gripper composed of three variable‐stiffness fingers with the antagonistic mechanism is proposed. Each finger can change its joint stiffness by selectively applying pretension to the springs so that it can exhibit different stiffness gradients to conform to the surfaces. Theoretical models are established to evaluate both the deformation and stiffness properties, and several experiments verify that it can increase the stiffness to 6.4 times after aggravating the antagonistic effect. Based on the variable stiffness, the finger performs sequential motion to conform to different curvatures, resulting in a larger contact area. Moreover, by stiffening joints, the proposed gripper can improve its grasping performance with a large grasping force while providing gentle contact. Furthermore, the gripper presents the capability of handling various objects with the optimal posture and gently grasping fragile objects like the paper ring and plasticine cylinder. Whereas this gripper can coordinate the relationship between high compliance and variable stiffness, the exploration of the gripper provides shed light on robotic design and practical application.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002