Traditional robotic mechanisms contain a series of rigid links connected by
rotational joints that provide powered motion, all of which is controlled by a
central processor. By contrast, analogous mechanisms found in nature, such as
octopus tentacles, contain sensors, actuators, and even neurons distributed
throughout the appendage, thereby allowing for motion with superior complexity,
fluidity, and reaction time. Smart materials provide a means with which we can
mimic these features artificially. These specialized materials undergo shape
change in response to changes in their environment. Previous studies have
developed material-based actuators that could produce targeted shape changes.
Here we extend this capability by introducing a novel computational and
experimental method for design and synthesis of material-based morphing
mechanisms capable of achieving complex pre-programmed motion. By combining
active and passive materials, the algorithm can encode the desired movement
into the material distribution of the mechanism. We demonstrate this new
capability by de novo design of a 3D printed self-tying knot. This method
advances a new paradigm in mechanism design that could enable a new generation
of material-driven machines that are lightweight, adaptable, robust to damage,
and easily manufacturable by 3D printing