RONov 2, 2021

Getting a Grip: in Materio Evolution of Membrane Morphology for Soft Robotic Jamming Grippers

arXiv:2111.01952v117 citations
Originality Incremental advance
AI Analysis

This research addresses the design of soft robotic grippers for improved gripping performance, representing an incremental advance by focusing on a previously unexplored parameter.

The study tackled the optimization of membrane morphology for granular jamming grippers, using multi-material 3D printing and an evolutionary algorithm to achieve high-performance designs that significantly differ from standard morphologies and generalize well across test objects.

The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing bespoke gripping performance, however the other main contributing factor, membrane morphology, has not been studied due to its particular complexities in both accurate modelling and fabrication. This research presents the first study that optimises membrane morphology for granular jamming grippers, combining multi-material 3D printing and an evolutionary algorithm to search through a varied morphology design space in materio. Entire generations are printed in a single run and gripper retention force is tested and used as a fitness measure. Our approach is relatively scalable, circumvents the need for modelling, and guarantees the real-world performance of the grippers considered. Results show that membrane morphology is a key determinant of gripper performance. Common high performance designs are seen to optimise all three of the main identified mechanisms by which granular grippers generate grip force, are significantly different from a standard gripper morphology, and generalise well across a range of test objects.

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