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.

When faced with an unstructured environment filled with an unknown number and size of obstacles on a chaotic terrain, it can be a challenge to determine the best method for navigating and mapping the space. This problem, known as Simultaneous Localization and Mapping (SLAM), has typically been approached using vision-based solutions, but these solutions require clear visual conditions in order to function optimally. A different approach to sensing environments has been explored in soft robotic systems, specifically by sensing changes in the environment through sensing changes in the robot's configuration. Building on this idea, we introduce a method of mapping based on colliding with and deforming around obstacles using a soft, growing robot. Instead of avoiding obstacles, as is typically done to protect robots, we take advantage of the soft, growing robot's compliance in order to navigate through, and collect information about, the environment. Through the construction and testing of a geometry-based simulation, we analyzed the behavior and ef-fectiveness of this approach for mapping by generating random launch positions and collecting information from contacted obstacles and traversed regions. Through a myriad of randomly generated environments, we determine that: 1) the density of obstacles in an environment has minimal impact on mapping abilities and 2) at least 70% of each environment tested can be mapped by deploying 20 or fewer soft, growing 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.

While haptics research has traditionally focused on the fingertips and hands, other locations on the body provide large areas of skin that could be utilized to relay large-area haptic sensations. Researchers have thus developed wearable devices that use distributed vibrotactile actuators and distributed pneumatic force displays, but these methods have limitations. In prior work, we presented a novel actuation technique involving stacking pneumatic pouches and evaluated the actuator output. In this work, we developed a wearable haptic device using this actuation technique and evaluated how the actuator output is perceived. We conducted a user study with 20 participants to evaluate users' perception thresholds, ability to localize, and ability to detect differences in contact area and compare their perception using the stacked pneumatic pouch actuation to traditional single-layer pouch actuation. We also used our device with stacked pneumatic actuation in a demonstration of a haptic hug that replicates the dynamics, pressure profile, and mapping to the human back, showcasing how this actuation technique can be used to create novel haptic stimuli.

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.

Continuum and soft robots can leverage complex actuator shapes to take onuseful shapes while actuating only a few of their many degrees of freedom. Continuum robotsthat alsogrow increasethe range of potential shapes that can be actuated and enable easier access to constrained environments. Existing models for describing the complex kinematics involved in general actuation of continuum robots rely on simulation or well-behaved stress–strain relationships, but the nonlinear behavior of the thin-walled inflated-beams used in growing robots makes these techniques difficult to apply. Here, we derive kinematic models of single, generally routed tendon paths on a soft pneumatic backbone of inextensible but flexible material from geometric relationships alone. This allows for forward modeling of the resulting shapes with only knowledge of the geometry of the system. We show that this model can accurately predict the shape of the whole robot body and how the model changes with actuation type. We also demonstrate the use of this kinematic model for inverse design, where actuator designs are found based on desired final robot shapes. We deploy these designed actuators on soft pneumatic growing robots to show the benefits of simultaneous growth and shape change.

Pneumatic wearable haptic devices can provide distributed pressure feedback to human operators during robot teleoperation and in virtual and augmented reality. However, these devices have an inherent trade-off between the spatial coverage of their pressure output and their resolution and dynamic response. To achieve specified spatial resolution and dynamic response, we propose a macro-mini actuation approach that stacks a number of smaller inflatable pouches atop a larger inflatable pouch. We develop models for the static and dynamic responses of single and stacked pouches and compare these with experimental results, providing guidelines for the design of wearable stacked pneumatic displays. Finally, we demonstrate this pneumatic macro-mini approach by replicating the time series pressure profiles of data collected from a huggable robot embedded with distributed force sensors.

Soft growing robots are proposed for use in applications such as complex manipulation tasks or navigation in disaster scenarios. Safe interaction and ease of production promote the usage of this technology, but soft robots can be challenging to teleoperate due to their unique degrees of freedom. In this paper, we propose a human-centered interface that allows users to teleoperate a soft growing robot for manipulation tasks using arm movements. A study was conducted to assess the intuitiveness of the interface and the performance of our soft robot, involving a pick-and-place manipulation task. The results show that users were able to complete the task 97% of the time and achieve placement errors below 2 cm on average. These results demonstrate that our body-movement-based interface is an effective method for control of a soft growing robot manipulator.

Pneumatically operated soft growing robots that extend via tip eversion are well-suited for navigation in confined spaces. Adding the ability to interact with the environment using sensors and tools attached to the robot tip would greatly enhance the usefulness of these robots for exploration in the field. However, because the material at the tip of the robot body continually changes as the robot grows and retracts, it is challenging to keep sensors and tools attached to the robot tip during actuation and environment interaction. In this paper, we analyze previous designs for mounting to the tip of soft growing robots, and we present a novel device that successfully remains attached to the robot tip while providing a mounting point for sensors and tools. Our tip mount incorporates and builds on our previous work on a device to retract the robot without undesired buckling of its body. Using our tip mount, we demonstrate two new soft growing robot capabilities: (1) pulling on the environment while retracting, and (2) retrieving and delivering objects. Finally, we discuss the limitations of our design and opportunities for improvement in future soft growing robot tip mounts.

Soft robotics and pneumatic actuation present opportunities for lightweight wearable haptic devices that provide distributed touch feedback to the skin. Ideally, such devices would be easily donned and doffed, since permanent coverage of a large area of the skin is undesirable. Here we present the design and evaluation of a concept device called HapWRAP: a growing haptic device constructed from flexible low density polyethylene. Controlled air flow through tubes and pouches allows HapWRAP to grow out of a compact housing unit and provide a combination of directional and force feedback to a user. When activated, HapWRAP grows up and around the forearm; its loops form a temporary sleeve. After growth, pneumatic actuators inflate and deflate to stimulate mechanoreceptors in the skin at distinguishable locations. This paper describes the design and manufacturing of HapWRAP, reports its performance metrics, and tests its suitability as a haptic feedback device. Participants were able to interpret force and direction cues from HapWRAP with 92.5% accuracy. These findings suggest that HapWRAP can be successfully used for applications where both force and direction cues are necessary.

For many types of robots, avoiding obstacles is necessary to prevent damage to the robot and environment. As a result, obstacle avoidance has historically been an important problem in robot path planning and control. Soft robots represent a paradigm shift with respect to obstacle avoidance because their low mass and compliant bodies can make collisions with obstacles inherently safe. Here we consider the benefits of intentional obstacle collisions for soft robot navigation. We develop and experimentally verify a model of robot-obstacle interaction for a tip-extending soft robot. Building on the obstacle interaction model, we develop an algorithm to determine the path of a growing robot that takes into account obstacle collisions. We find that obstacle collisions can be beneficial for open-loop navigation of growing robots because the obstacles passively steer the robot, both reducing the uncertainty of the location of the robot and directing the robot to targets that do not lie on a straight path from the starting point. Our work shows that for a robot with predictable and safe interactions with obstacles, target locations in a cluttered, mapped environment can be reached reliably by simply setting the initial trajectory. This has implications for the control and design of robots with minimal active steering.

There is currently a scarcity of wearable robotic devices that can practically provide physical assistance in a range of real world activities. Soft wearable exoskeletons, or exosuits, have the potential to be more portable and less restrictive than their rigid counterparts. In this paper, we present the design of an actuation system that has been optimized for use in a soft exosuit for the human arm. The selected design comprises a DC motor and gearbox, a flexible cable conduit transmission, and a custom series elastic force sensor. Placed in series with the transmission conduit, the custom compliant force sensor consists of a translational steel compression spring with a pair of Hall effect sensors for measuring deflection. The custom sensor is validated as an accurate means of measuring cable tension, and it is shown that it can be used in feedback to control the cable tension with high bandwidth. The dynamic effect of the cable-conduit transmission on the force felt at the user interface is characterized by backdriving the system as it renders a range of virtual impedances to the user. We conclude with recommendations for the integration of such an actuation system into a full wearable exosuit.