  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.
