Automatic design of fiber-reinforced soft actuators for trajectory matching

Significance Fluid-powered elastomeric soft robots have been shown to be able to generate complex output motion using a simple control input such as pressurization of a working fluid. This capability, which mimics similar functions often found in biology, results from variations in mechanical properties of the soft robotic body that cause it to strain to different degrees when stress is applied with the fluid. In this work, we outline a mechanics- and optimization-based approach that enables the automatic selection of mechanical properties of a fiber-reinforced soft actuator to match the kinematic trajectory of the fingers or thumb during a grasping operation. This methodology can be readily extended to other applications that require mimicking or assisting biological motions. Soft actuators are the components responsible for producing motion in soft robots. Although soft actuators have allowed for a variety of innovative applications, there is a need for design tools that can help to efficiently and systematically design actuators for particular functions. Mathematical modeling of soft actuators is an area that is still in its infancy but has the potential to provide quantitative insights into the response of the actuators. These insights can be used to guide actuator design, thus accelerating the design process. Here, we study fluid-powered fiber-reinforced actuators, because these have previously been shown to be capable of producing a wide range of motions. We present a design strategy that takes a kinematic trajectory as its input and uses analytical modeling based on nonlinear elasticity and optimization to identify the optimal design parameters for an actuator that will follow this trajectory upon pressurization. We experimentally verify our modeling approach, and finally we demonstrate how the strategy works, by designing actuators that replicate the motion of the index finger and thumb.