Fish Mouth Inspired Origami Gripper for Robust Multi-Type Underwater Grasping
Guo, Honghao, Huang, Junda, Zhang, Ian, Liang, Boyuan, Ma, Xin, Liu, Yunhui, Zhou, Jianshu
–arXiv.org Artificial Intelligence
Robotic grasping and manipulation in underwater environments present unique challenges for robotic hands traditionally used on land. These challenges stem from dynamic water conditions, a wide range of object properties from soft to stiff, irregular object shapes, and varying surface frictions. One common approach involves developing finger-based hands with embedded compliance using underactuation and soft actuators. This study introduces an effective alternative solution that does not rely on finger-based hand designs. We present a fish mouth inspired origami gripper that utilizes a single degree of freedom to perform a variety of robust grasping tasks underwater. The innovative structure transforms a simple uniaxial pulling motion into a grasping action based on the Yoshimura crease pattern folding. The origami gripper offers distinct advantages, including scalable and optimizable design, grasping compliance, and robustness, with four grasping types: pinch, power grasp, simultaneous grasping of multiple objects, and scooping from the seabed. In this work, we detail the design, modeling, fabrication, and validation of a specialized underwater gripper capable of handling various marine creatures, including jellyfish, crabs, and abalone. By leveraging an origami and bio-inspired approach, the presented gripper demonstrates promising potential for robotic grasping and manipulation in underwater environments.
arXiv.org Artificial Intelligence
Mar-20-2025
- Country:
- Africa
- Eritrea (0.04)
- Middle East
- Sudan (0.04)
- Asia
- China > Hong Kong (0.04)
- Middle East
- Saudi Arabia (0.04)
- Yemen (0.04)
- Europe > Italy
- Indian Ocean > Red Sea (0.04)
- North America > United States
- California > Alameda County > Berkeley (0.04)
- Africa
- Genre:
- Research Report (1.00)
- Technology:
- Information Technology > Artificial Intelligence > Robots > Manipulation (0.72)