Similarities between protein folding and granular jamming.

Grains and glasses, widely different materials, arrest their motions upon decreasing temperature and external load, respectively, in common ways, leading to a universal jamming phase diagram conjecture. However, unified theories are lacking, mainly because of the disparate nature of the particle interactions. Here we demonstrate that folded proteins exhibit signatures common to both glassiness and jamming by using temperature- and force-unfolding molecular dynamics simulations. Upon folding, proteins develop a peak in the interatomic force distributions that falls on a universal curve with experimentally measured forces on jammed grains and droplets. Dynamical signatures are found as a dramatic slowdown of stress relaxation upon folding. Together with granular similarities, folding is tied not just to the jamming transition, but a more nuanced picture of anisotropy, preparation protocol and internal interactions emerges. Results have implications for designing stable polymers and can open avenues to link protein folding to jamming theory

A Comprehensive Dataset of Grains for Granular Jamming in Soft Robotics: Grip Strength and Shock Absorption

We test grip strength and shock absorption properties of various granular material in granular jamming robotic components. The granular material comprises a range of natural, manufactured, and 3D printed material encompassing a wide range of shapes, sizes, and Shore hardness. Two main experiments are considered, both representing compelling use cases for granular jamming in soft robotics. The first experiment measures grip strength (retention force measured in Newtons) when we fill a latex balloon with the chosen grain type and use it as a granular jamming gripper to pick up a range of test objects. The second experiment measures shock absorption properties recorded by an Inertial Measurement Unit which is suspended in an envelope of granular material and dropped from a set height. Our results highlight a range of shape, size and softness effects, including that grain deformability is a key determinant of grip strength, and interestingly, that larger grain sizes in 3D printed grains create better shock absorbing materials

Fragility and hysteretic creep in frictional granular jamming

The granular jamming transition is experimentally investigated in a two-dimensional system of frictional, bi-dispersed disks subject to quasi-static, uniaxial compression at zero granular temperature. Currently accepted results show the jamming transition occurs at a critical packing fraction $\phi_c$. In contrast, we observe the first compression cycle exhibits {\it fragility} - metastable configuration with simultaneous jammed and un-jammed clusters - over a small interval in packing fraction ($\phi_1 < \phi < \phi_2$). The fragile state separates the two conditions that define $\phi_c$ with an exponential rise in pressure starting at $\phi_1$ and an exponential fall in disk displacements ending at $\phi_2$. The results are explained through a percolation mechanism of stressed contacts where cluster growth exhibits strong spatial correlation with disk displacements. Measurements with several disk materials of varying elastic moduli $E$ and friction coefficients $\mu$, show friction directly controls the start of the fragile state, but indirectly controls the exponential slope. Additionally, we experimentally confirm recent predictions relating the dependence of $\phi_c$ on $\mu$. Under repetitive loading (compression), the system exhibits hysteresis in pressure, and the onset $\phi_c$ increases slowly with repetition number. This friction induced hysteretic creep is interpreted as the granular pack's evolution from a metastable to an eventual structurally stable configuration. It is shown to depend upon the quasi-static step size $\Delta \phi$ which provides the only perturbative mechanism in the experimental protocol, and the friction coefficient $\mu$ which acts to stabilize the pack.Comment: 12 pages, 10 figure

Granular jamming based controllable organ design for abdominal palpation

Medical manikins play an essential role in the training process of physicians. Currently, most available simulators for abdominal palpation training do not contain controllable organs for dynamic simulations. In this paper, we present a soft robotics controllable liver that can simulate various liver diseases and symptoms for effective and realistic palpation training. The tumors in the liver model are designed based on granular jamming with positive pressure, which converts the fluid-like impalpable particles to a solid-like tumor state by applying low positive pressure on the membrane. Through inflation, the tumor size, liver stiffness, and liver size can be controlled from normal liver state to various abnormalities including enlarged liver, cirrhotic liver, and multiple cancerous and malignant tumors. Mechanical tests have been conducted in the study to evaluate the liver design and the role of positive pressure granular jamming in tumor simulations

A variable stiffness soft gripper using granular jamming and biologically inspired pneumatic muscles

As the domains in which robots operate change the objects a robot may be required to grasp and manipulate are likely to vary significantly and often. Furthermore there is increasing likelihood that in the future robots will work collaboratively alongside people. There has therefore been interest in the development of biologically inspired robot designs which take inspiration from nature. This paper presents the design and testing of a variable stiffness, three fingered soft gripper which uses pneumatic muscles to actuate the fingers and granular jamming to vary their stiffness. This gripper is able to adjust its stiffness depending upon how fragile/deformable the object being grasped is. It is also lightweight and low inertia making it better suited to operation near people. Each finger is formed from a cylindrical rubber bladder filled with a granular material. It is shown how decreasing the pressure inside the finger increases the jamming effect and raises finger stiffness. The paper shows experimentally how the finger stiffness can be increased from 21 to 71 N/m. The paper also describes the kinematics of the fingers and demonstrates how they can be position-controlled at a range of different stiffness values

Multi-fingered haptic palpation utilizing granular jamming stiffness feedback actuators

This paper describes a multi-fingered haptic palpation method using stiffness feedback actuators for simulating tissue palpation procedures in traditional and in robot-assisted minimally invasive surgery. Soft tissue stiffness is simulated by changing the stiffness property of the actuator during palpation. For the first time, granular jamming and pneumatic air actuation are combined to realize stiffness modulation. The stiffness feedback actuator is validated by stiffness measurements in indentation tests and through stiffness discrimination based on a user study. According to the indentation test results, the introduction of a pneumatic chamber to granular jamming can amplify the stiffness variation range and reduce hysteresis of the actuator. The advantage of multi-fingered palpation using the proposed actuators is proven by the comparison of the results of the stiffness discrimination performance using two-fingered (sensitivity: 82.2%, specificity: 88.9%, positive predicative value: 80.0%, accuracy: 85.4%, time: 4.84 s) and single-fingered (sensitivity: 76.4%, specificity: 85.7%, positive predicative value: 75.3%, accuracy: 81.8%, time: 7.48 s) stiffness feedback

GRAINS: Proximity Sensing of Objects in Granular Materials

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom

Characterization and Modeling of Granular Jamming: Models for Mechanical Design

peer reviewedThe use of granular jamming is proposed for designing structures with tunable rigidity of their tools (with the ability of being flexible devices for shaping and deformation but rigid for shape-locking and force transmission). The granular jamming consists in modifying the apparent rigidity of a structure by controlling the vacuum in a membrane filled with granular material. When the difference of pressure is low, the grains are free to move with respect to each other and the structure is flexible. When the vacuum in the membrane is increased, the grains are blocked and the structure is more rigid. Different mechanical characterizations of the granular jamming have been performed (triaxial compression and tension and cantilever beam bending tests) for different glass bead sizes ranging between 100 μm and 1 mm (used as granular material) at different vacuum levels (between 0 kPa and 90 kPa ). The grain size slightly influences the stiffness while the pressure difference is the main parameter to tune the stiffness of the structure. Based on these experiments, analytical models have been developed and validated. The tension characteristics can be directly deduced from the compression behavior and the bending modulus can be obtained by a combination of the tension and compression moduli. The proposed analytical models present the advantage of a simple formulation and are suitable for estimating the performance of other structures based on the granular jamming. The models can estimate and predict satisfactorily the results of granular jamming and can be used for designing mechanical structures based on this mechanism

An Abdominal Phantom with Tunable Stiffness Nodules and Force Sensing Capability for Palpation Training

Robotic phantoms enable advanced physical examination training before using human patients. In this paper, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This paper makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-means-quare error (RMSE) of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) MOTION grant EP/N03211X/2 and EP/N03208X/1, and EPSRC RoboPatient grant EP/T00603X/