Harnessing Nonuniform Pressure Distributions in Soft Robotic Actuators

Author:

Matia Yoav12ORCID,Kaiser Gregory H.1,Shepherd Robert F.1,Gat Amir D.3ORCID,Lazarus Nathan4ORCID,Petersen Kirstin H.1ORCID

Affiliation:

1. College of Engineering Cornell University Ithaca NY 14853 USA

2. ORAU Fellowship Program at Army Research Lab Adelphi MD 20783 USA

3. Mechanical Engineering Technion - Israel Institute of Technology Technion City Haifa 3200003 Israel

4. Electrical and computer engineering University of Delaware 139 The Green Newark DE 19716 USA

Abstract

Herein, complex motion in soft, fluid‐driven actuators composed of elastomer bladders arranged around a neutral plane and connected by slender tubes is demonstrated. Rather than relying on complex feedback control or multiple inputs, the motion is generated with a single pressure input, leveraging viscous flows within the actuator to produce nonuniform pressure between bladders. Using an accurate predictive model coupling with a large deformation Cosserat rod model and low‐Reynolds‐number flow, all dominating dynamic interactions including extension and curvature are captured with two governing equations. Given insights from this model, five design elements are described and demonstrated in practice. By choosing the relative timescales between the solid, fluid, and input pressure cycles, the tip of the actuator can obtain almost any desired trajectory and can be placed anywhere temporarily within its 2D workspace. Finally, the benefits of viscous‐driven soft actuators are showcased in a six‐legged untethered robot able to walk 0.05 body lengths per second. The foundation is laid for a new class of morphologically intelligent, soft robotic actuators that enables complex deformations and multifunctionality without explicit drivers; whereby generating nonuniform pressure distributions, their infinite degrees of freedom can be exploited.

Funder

Army Research Laboratory

National Science Foundation

David and Lucile Packard Foundation

Publisher

Wiley

Subject

General Medicine

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