Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors.

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Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics.

Emerging interest to synthesize active, engineered matter suggests a future where smart material systems and structures operate autonomously around people, serving diverse roles in engineering, medical, and scientific applications Similar to biological organisms, a realization of active, engineered matter necessitates functionality culminating from a combination of sensory and control mechanisms in a versatile material frame Recently, metamaterial platforms with integrated sensing and control have been exploited, so that outstanding non-natural material behaviors are empowered by synergistic microstructures and controlled by smart materials and systems This emerging body of science around active mechanical metamaterials offers a first glimpse at future foundations for autonomous engineered systems referred to here as soft, smart matter Using natural inspirations, synergy across disciplines, and exploiting multiple length scales as well as multiple physics, researchers are devising compelling exemplars of actively controlled metamaterials, inspiring concepts for autonomous engineered matter While scientific breakthroughs multiply in these fields, future technical challenges remain to be overcome to fulfill the vision of soft, smart matter This Review surveys the intrinsically multidisciplinary body of science targeted to realize soft, smart matter via innovations in active mechanical metamaterials and proposes ongoing research targets that may deliver the promise of autonomous, engineered matter to full fruition

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202001384

Foundations for Soft, Smart Matter by Active Mechanical Metamaterials.

Soft robots are challenging to model due in large part to the nonlinear properties of soft materials. Fortunately, this softness makes it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. This paper implements and evaluates a system identification method based on Koopman operator theory in which models of nonlinear dynamical systems are constructed via linear regression of observed data by exploiting the fact that every nonlinear system has a linear representation in the infinite-dimensional space of real-valued functions called observables. The approach does not suffer from some of the shortcomings of other nonlinear system identification methods, which typically require the manual tuning of training parameters and have limited convergence guarantees. A dynamic model of a pneumatic soft robot arm is constructed via this method, and used to predict the behavior of the real system. The total normalized-root-mean-square error (NRMSE) of its predictions is lower than that of several other identified models including a neural network, NLARX, nonlinear Hammerstein-Wiener, and linear state space model.

Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory

This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or rotary encoders, are difficult to adapt to robots that lack discrete mechanical joints. The rigid nature of these sensors runs contrary to the aspirational benefits of soft systems. As truly soft sensor solutions are still in their infancy, motion control of soft robots has so far relied on laboratory-based sensing systems such as motion capture, electromagnetic (EM) tracking, or Fiber Bragg Gratings. In this article, we used embedded flexible sensors known as Smart Braids to sense the contraction of McKibben muscles through changes in inductance. We evaluated closed-loop control on two systems: a revolute joint and a planar, one degree of freedom continuum manipulator. In the revolute joint, our proposed controller compensated for elasticity in the actuator connections. The Smart Braid feedback allowed motion control with a steady-state root-mean-square (RMS) error of [1.5]°. In the continuum manipulator, Smart Braid feedback enabled tracking of the desired tip angle with a steady-state RMS error of [1.25]°. This work demonstrates that Smart Braid sensors can provide accurate position feedback in closed-loop motion control suitable for field applications of soft robotic systems.

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Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

Over the past few years, modeling of continuum robots has been the subject of considerable attention in the research community. In this paper, we compare a set of forward kinematic models developed for continuum robots, with the underlying assumption of piecewise constant curvature. A new approximate kinematic model based on phase and actuator length differences is also introduced for comparison. The comparative evaluation consists of computer simulation and physical experiments on a multisection continuum robotic manipulator, the OctArm. The experiments include both elongation and bending in 3D space. The comparative accuracy of the models is reported, along with relative numerical stability. Further conclusions are drawn on the applicability of the models to different real-world scenarios.

A Comparison of Constant Curvature Forward Kinematics for Multisection Continuum Manipulators

In this work we present a novel, inductance-based system to measure and control the motion of bellows-driven continuum joints in soft robots. The sensing system relies on coils of wire wrapped around the minor diameters of each bellows on the joint. As the bellows extend, these coils of wire become more distant, decreasing their mutual inductance. Measuring this change in mutual inductance allows us to measure the motion of the joint. By dividing the sensing of the joint into two sections and measuring the motion of each section independently, we are able to measure the overall deformation of the joint with a piece-wise constant-curvature approximation. This technique allows us to measure lateral displacements that would be otherwise unobservable. When measuring bending, the inductance sensors measured the joint orientation with an RMS error of 1.1 °. The inductance sensors were also successfully used as feedback to control the orientation of the joint. The sensors proposed and tested in this work provided accurate motion feedback that would be difficult to achieve robustly with other sensors. This sensing system enables the creation of robust, self-sensing soft robots based on bellows-driven continuum joints.

An inductance-based sensing system for bellows-driven continuum joints in soft robots

http://www.roboticsproceedings.org/rss13/p27.pdf

An Inductance-Based Sensing System for Bellows-Driven Continuum Joints in Soft Robots

A fluidic robotic actuator configured to assume at least a neutral position, the fluidic robotic actuator includes a first and second plate defining respective planar portions that are disposed in parallel planes in the neutral position. The fluidic robotic actuator also includes a plurality of elongated bellows extending between the first and second plates, the bellows each having a central main axis that is parallel to the central main axis of the other bellows in the neutral position, the main axis of the bellows being perpendicular to the parallel planes of the first and second plates in the neutral position, the bellows being coupled to the first and second plates at respective first and second ends of the bellows.

Fluidic robotic actuator system and method

A poppet valve system that includes a valve body having a first and second end and defining a valve cavity and a poppet assembly extending from the first end to the second end and through the valve cavity. The poppet assembly can include a movable poppet head disposed at the first end of the poppet assembly and configured generate a seal by contacting a portion of the valve body and configured to define an opening between the valve cavity and a pressure cavity. The poppet assembly can also include a movable guiding element disposed at the second end of the valve body and a shaft extending from the first end to the second end and coupled to the poppet head and guiding element.

/pdf/poppet-valve-system-and-method-1zb06t92eb.pdf

Maria J. Telleria

Papers

An inductance-based sensing system for bellows-driven continuum joints in soft robots

An Inductance-Based Sensing System for Bellows-Driven Continuum Joints in Soft Robots

Fluidic robotic actuator system and method

Poppet valve system and method