Tentacle robot can gently grasp fragile objects: jellyfish soft gripper mimics the mechanics of curly hair

If you’ve ever played the claw game in an arcade, you know how difficult it is to grab and hold objects with robot grippers. Imagine how much more unnerving that game would be if, instead of plush stuffed animals, you tried to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship.

Most of today’s robotic grippers rely on built-in sensors, complex feedback loops or advanced machine learning algorithms, combined with the skill of the operator, to grip fragile or irregularly shaped objects. But researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have shown a simpler way.

Inspired by nature, they designed a new type of soft robotic grabber that uses a collection of thin tentacles to ensnare and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles or filaments are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap objects and requires no detection, planning or feedback control.

The research is published in the Proceedings of the National Academy of Sciences (PNAS).

“With this research, we wanted to rethink how we interact with objects,” said Kaitlyn Becker, former graduate student and postdoctoral researcher at SEAS and lead author of the paper. “By leveraging the natural flexibility of soft robotics and enhancing it with a flexible structure, we designed a gripper that is greater than the sum of its parts and a gripping strategy that can adapt to a range of complex objects with minimal planning and perception.”

Becker is currently an Assistant Professor of Mechanical Engineering at MIT.

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The grab’s strength and adaptability stems from its ability to entangle itself in the object it is trying to grab. The foot-long filaments are hollow, rubber tubes. One side of the tube has thicker rubber than the other, so when the tube is under pressure, it curls like a ponytail or straight hair on a rainy day.

The curls knot and intertwine with each other and the object, with each entanglement increasing the strength of the hold. While the collective grip is strong, each contact individually is weak and will not damage even the most fragile object. To release the object, the filaments are simply depressurized.

The researchers used simulations and experiments to test the gripper’s efficacy, picking up a range of objects, including various houseplants and toys. The gripper can be used in real applications for gripping soft fruits and vegetables for agricultural production and distribution, delicate tissue in medical environments, even irregularly shaped objects in warehouses such as glassware.

This new approach to gripping combines Professor L. Mahadevan’s research on the topological mechanics of entangled filaments with Professor Robert Wood’s research on soft robotic grippers.

“Entanglement allows any highly flexible filament to conform locally to a target object, leading to a secure yet soft topological grip that is relatively independent of the details of the nature of the contact,” said Mahadevan, the Lola England de Valpine professor of Applied Mathematics in SEAS, and of Organismic and Evolutionary Biology, and Physics in FAS and co-corresponding author of the paper.

“This new approach to robotic gripping complements existing solutions by replacing simple, traditional grippers that require complex control strategies with extremely conformal and morphologically complex filaments that can operate with very simple control,” said Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering. and Applied Sciences and co-corresponding author of the paper. “This approach expands the range of what is possible to pick up with robotic grippers.”

The study was co-authored by Clark Teeple, Nicholas Charles, Yeonsu Jung, Daniel Baum and James C. Weaver. It was supported in part by the Office of Naval Research, under grant N00014-17-1-206 and the National Science Foundation under grants EFRI-1830901, DMR-1922321, DMR-2011754, DBI-1556164 and EFMA-1830901 and the Simons Foundation and the Henri Seydoux Fund.


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