This paper presents the design of a new soft pneumatic actuator whose direction and magnitude of bending may be precisely controlled via activation of different shape memory alloy (SMA) springs within the actuator, in conjunction with pneumatic actuation. This design is inspired by examples seen in nature such as the human tongue, where the combination of hydrostatic pressure and contraction of intrinsic muscle groups enables precise maneuverability and morphing capabilities. Here, SMA springs are embedded in the walls of the actuator, serving as intrinsic muscles that may be selectively activated to constrain the device. The pneumatic SMA (PneuSMA) actuator demonstrates remarkable spatial controllability evidenced by testing under different pressures and SMA activation combinations. A baseline finite element model is also developed to predict the actuator deformation under different pressure and activation conditions.

Steerable needles have been widely researched in recent years, and they have multiple potential roles in the medical area. The flexibility and capability of avoiding obstacles allow the steerable needles to be applied in the biopsy, drug delivery and other medical applications that require a high degree of freedom and control accuracy. Radius of Curvature (ROC) of the needle while inserting in the soft tissue is an important parameter for evaluation of the efficacy, and steerability of these flexible needles. For our Fracture-directed Stylet-and-Tube Steerable Needles, it is important to find a relationship among the resultant insertion ROC, pre-set wire shape and the Young's Modulus of soft tissue to characterize this class of steerable needles. In this paper, an approach is provided for obtaining resultant ROC using stylet and tissue's mechanical properties. A finite element analysis is also conducted to support the reliability of the model. This work sets the foundation for other researchers to predict the insertion ROC based on the mechanical properties of the needle, and the soft tissue that is being inserted.

This paper presents an approach for modeling new soft robotic materials which possess the ability to control directional stiffness. These materials are inspired by biological systems where movements are enabled by variable stiffness tissue and contraction of localized muscle groups. Here a low-melting-point (LMP) material lattice embedded in an elastomer serves as a rigid skeleton that may be locally melted to allow bending at selectable joint locations. The forward kinematics of the lattice has been modeled using the product of exponentials method with the incorporation of bending axis selectivity. In this paper, we develop this model to account for torques imposed by tendons, and we model the elastomer's resistance to bending as a torsional spring at the selected joints. Thus we obtain a two-way relationship between tendon forces and joint angles/axes. The concept of applying traditional robot modeling strategies to selectively compliant robotic structures could enable precise control of dexterous soft robots that satisfy stringent safety criteria.

The ability to change stiffness is a capability exhibited through the animal kingdom, with many recent advances in tunable stiffness in the area of robotics. In this paper, we propose a mechanism design that provides the ability to make modular subcomponents with tunable stiffness by creating a bi-stable mechanism that exhibits different stiffnesses in each of the stable configurations. The design is based on controlled buckling of two linear springs in series and allows design-time control over the stiffnesses, equilibrium points, and energy required to transition between the stable configurations.

In this paper, we present the design of a shape-memory-alloy (SMA)-based compliant linear actuator [active cell (AC)] and the use of these in designing and modeling articulated meshes, which form the mechanical subsystem of a class of proposed modular active-cell robots (MACROs). The ACs are capable of undergoing ~25% strain and groups of cells are connected via passively compliant nodes to produce articulated mesh networks. The deformation of compliant meshes of ACs is modeled by scale-invariant parametric equations derived from the physics of SMA deformations and a reduced-order model of the cells. Parameters of the implemented system were used to develop a simulation platform that predicts the mechanical deformation of the networked robot given electrical inputs at arbitrary nodes of the network. We provide results of several experimental trials used to validate and establish the accuracy of this deformation model. The error in predicting deformations in small meshes is shown to be under 10% over both time-varying inputs and at steady states.

While techniques exist for the rapid prototyping of mechanical and electrical components separately, this paper describes a method where commercial Additive Manufacturing (AM) techniques can be used to concurrently construct the mechanical structure and electronic circuits in a robotic or mechatronic system. The technique involves printing hollow channels within parts that are then filled with a low melting point liquid metal alloy that solidifies upon cooling to form electrical traces. This method is compatible with most conventional fused deposition modeling and stereolithography machines, and requires no modification to an existing printer, though the technique could easily be incorporated into multi-material machines. Three primary considerations are explored using the a commercial fused deposition manufacturing (FDM) process as a testbed: material and manufacturing process parameters, simplified injection fluid mechanics, and automatic part generation using standard printed circuit board software tools. As demonstration of the ability to embed circuit in RP parts, a differential-drive robot is printed, populated with discrete electronic components, and injected to create a fully functional robot.

We present the design of simple, centimeter-scale modular actuation units (“Active Cells”) and passive compliant nodes that are electromechanically networked to create macroscopically deformable Modular Active Cell-based Structures (MACROs). Each Active Cell is a single degree-of-freedom linear actuator (a “muscle unit”), consisting of fiberglass end-pieces connecting two strands of Nitinol shape-memory alloy and a passive biasing spring. The Nitinol strands are coiled into a tight spring to increase deformations when activated through resistive heating. In-depth examination of the optimization of Nitinol coils with an antagonistic spring is presented, resulting in large repeatable axial cell strains of up to 25%. The design of these cellular muscle units to obtain maximal repeatable stroke is presented, allowing for the construction of larger networks of cells (MACRO modules, akin to a biological “tissue”) that can be customized to a target application. Finally, experimental demonstration of the construction and actuation of some simple MACRO modules is described.

While there has been a steady progression of research in robotic and mechatronic systems that utilize nickel titanium alloy (Nitinol) as an actuator, the design of the antagonistic element for the inherently “one-way” technology has not been thoroughly investigated and described. In this paper, we discuss the properties of Nitinol-based shape memory alloy actuators as they relate to the design of passive spring antagonists. We describe the major classes of design goals as they relate to the choice of properties of the antagonistic element, and present techniques for optimizing parallel antagonists through passive linear springs in order to maximize the generally most desirable property of the actuator - the maximal repeatable strain of the antagonist pair.

The proposed research effort explores the development of active cells - simple contractile electromechanical units that can be used as the material basis for larger articulable structures. Each cell, which might be considered a “muscle unit”, consists of a contractile Nitinol SMA core with conductive terminals. Large numbers of these cells might be combined and externally powered to change phase, contracting to either articulate with a large strain or increase the stiffness of the ensemble, depending on the cell design. Unlike traditional work in modular robotics, the approach presented here focuses on cells that have a simplistic design and function, are inexpensive to fabricate, and are eventually scalable to sub-millimeter sizes, working towards our vision of robot structures that can be custom-fabricated from large numbers of general cell units, similar to biological structures.

Needle insertions serve a critical role in a wide variety of medical interventions. Steerable needles provide a means by which to enhance existing percutaneous procedures and afford the development of entirely new ones. Here, we present a new time-varying model for the torsional dynamics of a steerable needle, along with a new controller that takes advantage of the model. The torsional model incorporates time-varying mode shapes to capture the changing boundary conditions caused during insertion of the needle into the tissue. Extensive simulations demonstrate the improvement over a model that neglects torsional dynamics, and illustrates the possible effect of torsional model order on efficacy. Pilot feedback control experiments, conducted in artificial tissue (plastisol) under stereo image guidance, validate the overall approach: our results substantially out-perform previously reported experimental results on controlling tip-steerable needles.

Traditionally, visual servoing is separated into tracking and control subsystems. This separation, though convenient, is not necessarily well justified. When tracking and control strategies are designed independently, it is not clear how to optimize them to achieve a certain task. In this work, we propose a framework in which spatial sampling kernels - borrowed from the tracking and registration literature - are used to design feedback controllers for visual servoing. The use of spatial sampling kernels provides natural hooks for Lyapunov theory, thus unifying tracking and control and providing a framework for optimizing a particular servoing task. As a first step, we develop kernel-based visual servos for a subset of relative motions between camera and target scene. The subset of motions we consider are 2D translation, scale, and roll of the target relative to the camera. Our approach provides formal guarantees on the convergence/stability of visual servoing algorithms under putatively generic conditions.