Soft robots are known for their ability to perform tasks with great adaptability, enabled by their distributed, non-uniform stiffness and actuation. Bending is the most fundamental motion for soft robot design, but creating robust, and easy-to-fabricate soft bending joint with tunable properties remains an active problem of research. In this work, we demonstrate an inflatable actuation module for soft robots with a defined bending plane enabled by forced partial wrinkling. This lowers the structural stiffness in the bending direction, with the final stiffness easily designed by the ratio of wrinkled and unwrinkled regions. We show the stiffness properties of the actuation module through a first-principle model validated by experimental characterization, and demonstrate the module's ability to maintain the kinematic constraint over a large range of loading conditions. We illustrate how these properties give the potential for complex actuation in a soft continuum robot and for decoupling actuation force and efficiency from load capacity. The module provides a novel method for embedding intelligent actuation into soft pneumatic robots.

Localization of robots is a complex task that is often hindered by the sensors these systems use. Due to the majority of field robots being rigid, most of these sensing modalities have the same common faults, such as performance being hindered when their camera vision is obscured. In addition, rigid systems lack flexibility when traversing multiple environments: especially when traversing uneven and unpredictable ground. Soft robots, which can adaptably interact with the environment, could serve as a solution to both problems. One specific soft robot, the Vine Robot, has exhibited excellent performance while moving through constrained, unpredictable environments. This makes the Vine Robot an ideal candidate for a novel method of sensing and localizing in environments, obstacle collision localization. We use our understanding of the nature of Vine Robot motion to be able to predict the tip position of the robot at every instant based on sensor feedback. Through the single obstacle experiments, it was found that our algorithm can provide a precise picture of the tip position of the robot in differing environments. Further, in a multi obstacle demonstration, less than 5% max error relative to the full robot length was observed on the path prediction. Our study helps lay the foundation for a new method for Vine Robot localization using contact as a new sensing modality.

As soft, continuum robots see increasing areas of application, many scenarios have arisen where it is necessary to consider the geometric shape of the robot. The current approaches to robot kinematics, such as the piecewise constant-curvature (PCC) model, are effective in representing simple overall robot geometry and estimating the end-effector state, but they are less intuitive for planning robots that involve complex geometries. In this work, we propose a solution to the geometric design problem by a two-part approach: a freeform spline defines a “shape curve” that describes the overall geometry of the robot, and then a “kinematic curve” composed of shapes that are feasible to replicate with continuum robots is fitted to the shape curve. As an implementation of this approach, we specifically explore the application of piecewise cubic Bezier curves in designing the shape curve of the robot, and pairs of arcs to construct the kinematic curves. Finally, the approach is applied to a tip-extension “vine” robot that is designed and fabricated to “grow” along a designed path and access the top surface of an obstacle.