Evolving soft robotic jamming grippers

Abstract

Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacuum pump and function by pressing the un-jammed gripper against a target object and evacuating the air to transition the material to a jammed (solid) state, gripping the target object. However, due to the complex interactions between grain morphology and target object shape, much uncertainty still remains regarding optimal constituent grain shapes for specific gripping applications. We address this challenge by utilising a modern Evolutionary Algorithm, NSGA-III, combined with a Discrete Element Method soft robot model to perform a multi-objective optimisation of grain morphology for use in jamming grippers for a range of target object sizes. Our approach optimises the microscopic properties of the system to elicit bespoke functional granular material performance driven by the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Results establish the important contribution of grain morphology to gripper performance and the critical role of local surface curvature and the length scale governed by the relative sizes of the grains and target object.