Much of the research on bioinspired soft robotics has focused on capturing the interplay of biological form and function. However, existing soft robotic actuators are mostly made with linear or planar fabrication orientations that do not represent the resting geometry of complex biological systems, such as curved musculature. This work introduces the ability to create fiber-reinforced actuators with precurved configurations. By tuning variables such as dimensions and fiber angles, an optimization algorithm can prescribe the mechanical fabrication parameters to create a fiber-reinforced actuator that can generate controlled motion to follow a desired input trajectory. Precurved configurations introduce an additional optimization parameter, the initial bend angle, allowing for a more accurate and robust algorithm and generating a median percent error of <1%. With a customized software tool, we can take existing motion data from biological systems—such as medical imaging—and build soft robotic actuators optimized to replicate these trajectories. We can predict the motion of precurved actuators both analytically and numerically and replicate the motion experimentally, with excellent trajectory matching between the three. In constructing actuators that better match the native forms found within biological systems, we find that precurved actuators are more efficient than their initially straight counterparts. This pneumatic efficiency allows for the use of control systems with lower power and precision, lowering the economic cost of the associated control hardware, while more accurately replicating the biological motion. Taking two examples from biology, that of the human diaphragm during respiration and that of a jellyfish bell during locomotion, we design and generate fiber reinforced actuators to mimic these motions.
- Control and Systems Engineering
- Artificial Intelligence