Introducing redundancy in multi-material, self-healing soft robots

Soft robots that consist out of flexible membranes, are susceptible to
damage. Accordingly, a healing ability was incorporated in these new
generation robots, by constructing them out of synthetic self-healing
(SH) polymers, specifically reversible Diels-Alder (DA) networks.
However, in these systems neither healing nor actuation is performed
in a controlled and autonomous manner, which is essential for the
efficient introduction of these SH soft robots in unstructured, dynamic
environments.

Therefore, smart controllers for both actuation and autonomous damage detection and healing will be developed. These control systems will include incorporated SH soft sensors manufactured out of conductive DA-networks. Combining materials with different properties in soft robotics can lead to complex behaviour and improved performance. However, multi-material interfaces mostly rely on weak adhesion, leading to a decrease in lifetime of the soft robots. In contrast, DA-polymers with divergent mechanical properties can be covalently bonded together, creating high strength interfaces. This merging ability will be exploited to produce robust multi-material soft robots, with improved actuation performance. As healing takes time, it would be more economical if the system can continue operation, while partially damaged, instead of being put temporarily offline. To make this possible, both
redundancy and learning algorithms for compensatory behaviour will be added to SH soft robots.