A Constitutive Model for Torsional Loads on Fluid-Driven Soft Robots

2017 ◽  
Author(s):  
Audrey Sedal ◽  
Daniel Bruder ◽  
Joshua Bishop-Moser ◽  
Ram Vasudevan ◽  
Sridhar Kota
Keyword(s):  
Constitutive Model ◽  
Large Scale ◽  
Experimental Parameter ◽  
Parameter Determination ◽  
Computationally Efficient ◽  
Computing Systems ◽  
Soft Robots ◽  
Soft Actuator ◽  
Kinematic Behaviour ◽  
Torsional Loads

For soft robots to be utilized in medical devices, locomotion, industrial automation, and a number of other fields, they require accurate and computationally efficient models that capture both the kinetic behavior and inherent non-linearity. Fiber reinforcement enables soft robots to create useful motions, such as rotation, but poses additional complexity in modeling. The purpose of this paper is to present a constitutive model that relates the kinematic behaviour of a pneumatic fiber-reinforced soft actuator with its torsional loading. This model has the advantage of requiring minimal experimental parameter determination, being inclusive of torsional loads, without requiring large-scale computing systems. Experimental data in multiple kinematic configurations shows agreement between the models moment prediction and the moments that the actuators generate.

A Continuum Model for Fiber-Reinforced Soft Robot Actuators

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Audrey Sedal ◽  
Daniel Bruder ◽  
Joshua Bishop-Moser ◽  
Ram Vasudevan ◽  
Sridhar Kota
Keyword(s):  
Continuum Model ◽  
Applied Pressure ◽  
Experimental Parameter ◽  
Building Blocks ◽  
Axial Rotation ◽  
Parameter Determination ◽  
Fiber Reinforced ◽  
Operational Parameters ◽  
Computationally Efficient ◽  
Design Synthesis

Fiber-reinforced elastomeric enclosures (FREEs) generate sophisticated motions, when pressurized, including axial rotation, extension, and compression, and serve as fundamental building blocks for soft robots in a variety of applications. However, most modeling techniques employed by researchers do not capture the key characteristics of FREEs to enable development of robust design and control schemes. Accurate and computationally efficient models that capture the nonlinearity of fibers and elastomeric components are needed. This paper presents a continuum model that captures the nonlinearities of the fiber and elastomer components as well as nonlinear relationship between applied pressure, deformation, and output forces and torque. One of the key attributes of this model is that it captures the behavior of FREEs in a computationally tractable manner with a minimum burden on experimental parameter determination. Without losing generality of the model, we validate it for a FREE with one fiber family, which is the simplest system exhibiting a combination of elongation and twist when pressurized. Experimental data in multiple kinematic configurations show agreement between our model prediction and the moments that the actuators generate. The model can be used to not only determine operational parameters but also to solve inverse problems, i.e., in design synthesis.

scholarly journals Comparison and experimental validation of predictive models for soft, fiber-reinforced actuators

2019 ◽  
pp. 027836491987949 ◽  
Author(s):  
Audrey Sedal ◽  
Alan Wineman ◽  
R. Brent Gillespie ◽  
C. David Remy
Keyword(s):  
Experimental Parameter ◽  
Mathematical Structure ◽  
Lumped Parameter Model ◽  
Peak Performance ◽  
Parameter Determination ◽  
Design Parameters ◽  
Neural Net ◽  
Fiber Reinforced ◽  
High Performing ◽  
Soft Actuator

Successful soft robot modeling approaches appearing in the recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been explicitly compared against each other. In this article, we show how three distinct model structures, a lumped-parameter model, a continuum mechanical model, and a neural network, compare in capturing the gross trends and specific features of the force generation of soft robotic actuators. In particular, we study models for fiber-reinforced elastomeric enclosures (FREEs), which are a popular choice of soft actuator and that are used in several soft articulated systems, including soft manipulators, exoskeletons, grippers, and locomoting soft robots. We generated benchmark data by testing eight FREE samples that spanned broad design and kinematic spaces and compared the models on their ability to predict the loading–deformation relationships of these samples. This comparison shows the predictive capabilities of each model on individual actuators and each model’s generalizability across the design space. While the neural net achieved the highest peak performance, the first principles-based models generalized best across all actuator design parameters tested. The results highlight the essential roles of mathematical structure and experimental parameter determination in building high-performing, generalizable soft actuator models with varying effort invested in system identification.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

scholarly journals A Mixed Numerical-Experimental Method to Characterize Metal-Polymer Interfaces for Crash Applications

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 818
Author(s):  
Jonas Richter ◽  
Moritz Kuhtz ◽  
Andreas Hornig ◽  
Mohamed Harhash ◽  
Heinz Palkowski ◽  
...  
Keyword(s):  
Experimental Parameter ◽  
Dissimilar Materials ◽  
Calibration Method ◽  
Parameter Determination ◽  
Failure Behavior ◽  
Cohesive Elements ◽  
Interface Failure ◽  
Wide Range ◽  
Polymer Metal ◽  
Metal Polymer

Metallic (M) and polymer (P) materials as layered hybrid metal-polymer-metal (MPM) sandwiches offer a wide range of applications by combining the advantages of both material classes. The interfaces between the materials have a considerable impact on the resulting mechanical properties of the composite and its structural performance. Besides the fact that the experimental methods to determine the properties of the single constituents are well established, the characterization of interface failure behavior between dissimilar materials is very challenging. In this study, a mixed numerical–experimental approach for the determination of the mode I energy release rate is investigated. Using the example of an interface between a steel (St) and a thermoplastic polyolefin (PP/PE), the process of specimen development, experimental parameter determination, and numerical calibration is presented. A modified design of the Double Cantilever Beam (DCB) is utilized to characterize the interlaminar properties and a tailored experimental setup is presented. For this, an inverse calibration method is used by employing numerical studies using cohesive elements and the explicit solver of LS-DYNA based on the force-displacement and crack propagation results.

scholarly journals A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5458
Author(s):  
Sangjun Kim ◽  
Kyung-Joon Park
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Large Scale ◽  
Computing System ◽  
Future Research ◽  
Physical Security ◽  
Computing Systems ◽  
Physical Systems ◽  
Security Research ◽  
Simultaneous Control

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

Large-Scale Spinning Approach to Engineering Knittable Hydrogel Fiber for Soft Robots

ACS Nano ◽  
2020 ◽  
Vol 14 (11) ◽  
pp. 14929-14938
Author(s):  
Xiangyu Duan ◽  
Jingyi Yu ◽  
Yaxun Zhu ◽  
Zhiqiang Zheng ◽  
Qihua Liao ◽  
...  
Keyword(s):  
Large Scale ◽  
Soft Robots

An Efficient Preconditioner for Linear System Solution in Multi-Domain Modeling of the Circulatory System

2013 ◽  
Author(s):  
Mahdi Esmaily Moghadam ◽  
Yuri Bazilevs ◽  
Tain-Yen Hsia ◽  
Alison Marsden
Keyword(s):  
Strong Coupling ◽  
Large Scale ◽  
Computational Cost ◽  
Circulatory System ◽  
Flow Simulation ◽  
Global Dynamics ◽  
Domain Modeling ◽  
Computationally Efficient ◽  
Lumped Parameter ◽  
System Solution

A closed-loop lumped parameter network (LPN) coupled to a 3D domain is a powerful tool that can be used to model the global dynamics of the circulatory system. Coupling a 0D LPN to a 3D CFD domain is a numerically challenging problem, often associated with instabilities, extra computational cost, and loss of modularity. A computationally efficient finite element framework has been recently proposed that achieves numerical stability without sacrificing modularity [1]. This type of coupling introduces new challenges in the linear algebraic equation solver (LS), producing an strong coupling between flow and pressure that leads to an ill-conditioned tangent matrix. In this paper we exploit this strong coupling to obtain a novel and efficient algorithm for the linear solver (LS). We illustrate the efficiency of this method on several large-scale cardiovascular blood flow simulation problems.

A Low-Fidelity Stochastic Model of Viral Spread in Aircraft to Assess Risk Mitigation Strategies

2021 ◽  
Author(s):  
Leigh McCue
Keyword(s):  
Large Scale ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Traffic Network ◽  
Computationally Efficient ◽  
Passenger Compartment ◽  
Stochastic Effects ◽  
Viral Spread ◽  
Risk Mitigation Strategies ◽  
Baseline Health

Abstract The purpose of this work is to develop a computationally efficient model of viral spread that can be utilized to better understand influences of stochastic factors on a large-scale system - such as the air traffic network. A particle-based model of passengers and seats aboard a single-cabin 737-800 is developed for use as a demonstration of concept on tracking the propagation of a virus through the aircraft's passenger compartment over multiple flights. The model is sufficiently computationally efficient so as to be viable for Monte Carlo simulation to capture various stochastic effects, such as number of passengers, number of initially sick passengers, seating locations of passengers, and baseline health of each passenger. The computational tool is then exercised in demonstration for assessing risk mitigation of intervention strategies, such as passenger-driven cleaning of seating environments and elimination of middle seating.

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