Deformation Modeling of Compliant Robotic Fingers Grasping Soft Objects

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Nicolas Mouazé ◽  
Lionel Birglen
Keyword(s):  
Experimental Testing ◽  
Simple Algorithm ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Dynamic Simulations ◽  
Potential Applications ◽  
Classical Models ◽  
Soft Objects ◽  
Efficient Approximation ◽  
Robotic Fingers

Abstract In this paper, a model is shown to predict the simultaneous deformations occurring when compliant robotic fingers are grasping soft objects. This model aims at providing an accurate estimation of the penetration, internal forces, and deformed shapes of both these fingers and the objects. A particular emphasis is placed on the case when the finger is underactuated but the methodology discussed in this paper is general. Usually in the literature, underactuated fingers are modeled and designed considering their grasps of rigid object because of the complexity associated with deforming objects. This limitation severely hinders the usability of underactuated grippers and either restricts them to a narrow range of applications or requires extensive experimental testing. Furthermore, classical models of underactuated fingers in contact with objects are typically applicable with a maximum of one contact per phalanx only. The model proposed in this paper demonstrates a simple algorithm to compute a virtual subdivision of the phalanges which can be used to estimate the contact pressure arising when there are contacts at many locations simultaneously. This model also proposes a computationally efficient approximation of isotropic soft objects. Numerical simulations of the proposed model are compared here with dynamic simulations, finite element analyses, and experimental measurements which all shows its effectiveness and accuracy. Finally, the extension of the model to other types of underactuated fingers, standard grippers, and fully actuated robotic fingers as well as potential applications is discussed and illustrated.

Diffusiophoresis of a charged drop

2018 ◽  
Vol 852 ◽  
pp. 37-59 ◽  
Author(s):  
Fan Yang ◽  
Sangwoo Shin ◽  
Howard A. Stone
Keyword(s):  
Analytical Solution ◽  
Oil Recovery ◽  
Electrolyte Solution ◽  
Solute Concentration ◽  
Solid Particles ◽  
Concentration Gradients ◽  
Charged Drop ◽  
Electric Stress ◽  
Potential Applications ◽  
Soft Objects

Diffusiophoresis describes the motion of colloids in an electrolyte or non-electrolyte solution where there is a concentration gradient. While most of the studies of diffusiophoresis focus on the motion of solid particles, soft objects such as drops and bubbles are also known to experience diffusiophoresis. Here, we investigate the diffusiophoresis of charged drops in an electrolyte solution both analytically and experimentally. The drop is assumed to remain spherical. An analytical solution of the diffusiophoretic velocity of drops is obtained by perturbation methods. We find that the flow inside the drop is driven by the tangential electric stress at the interface and it directly influences the diffusiophoretic speed of the drop. Using charged oil droplets, we measure the drop speed under solute concentration gradients and find good agreement with the analytical solution. Our findings have potential applications for oil recovery and drug delivery.

Surrogate articular contact models for computationally efficient multibody dynamic simulations

2010 ◽  
Vol 32 (6) ◽  
pp. 584-594 ◽  
Author(s):  
Yi-Chung Lin ◽  
Raphael T. Haftka ◽  
Nestor V. Queipo ◽  
Benjamin J. Fregly
Keyword(s):  
Computationally Efficient ◽  
Dynamic Simulations ◽  
Multibody Dynamic ◽  
Contact Models

scholarly journals PROBING COMPLEX NETWORKS FROM MEASURED TIME SERIES

2012 ◽  
Vol 22 (10) ◽  
pp. 1250236 ◽  
Author(s):  
LIANG HUANG ◽  
YING-CHENG LAI ◽  
MARY ANN F. HARRISON
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Network Topology ◽  
Efficient Method ◽  
Relative Importance ◽  
Computationally Efficient ◽  
Heuristic Argument ◽  
Principal Eigenvector ◽  
Measured Time ◽  
Potential Applications

We propose a method to detect nodes of relative importance, e.g. hubs, in an unknown network based on a set of measured time series. The idea is to construct a matrix characterizing the synchronization probabilities between various pairs of time series and examine the components of the principal eigenvector. We provide a heuristic argument indicating the existence of an approximate one-to-one correspondence between the components and the degrees of the nodes from which measurements are obtained. The striking finding is that such a correspondence appears to be quite robust, which holds regardless of the detailed node dynamics and of the network topology. Our computationally efficient method thus provides a general means to address the important problem of network detection, with potential applications in a number of fields.

A Nonlinear Dynamic Substructuring Approach to Global Dynamics of Flexible Riser Systems

2014 ◽  
Author(s):  
Arya Majed ◽  
Phil Cooper
Keyword(s):  
Dynamic Analysis ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Analysis ◽  
Nonlinear Effects ◽  
Global Dynamics ◽  
Computationally Efficient ◽  
Flexible Riser ◽  
Dynamic Simulations ◽  
Stick Slip ◽  
Global Dynamic

Standard riser global dynamic analysis software packages utilize line element models that cannot capture the complex behavior of flexible risers. This paper presents a computationally efficient nonlinear dynamic analysis methodology capable of incorporating detailed finite element models and scalable to global dynamic simulations of entire flexible riser systems. Subject methodology captures the global geometric nonlinear effects and its coupling to stick-slip friction — a clear requirement for accurate armour stress predictions. In addition, the method enables the formulation of stress transformation matrices which allow the direct recovery of armour stresses from the global simulations. A demonstration problem involving the nonlinear dynamic simulation of a 500m flexible riser system is presented.

scholarly journals Just-in-time Point Prediction Using a Computationally-efficient Lebesgue-sampling-based Prognostic Method

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Camilo Reyes ◽  
Francisco Jaramillo ◽  
Bin Zhang ◽  
Chetan Kulkarni ◽  
Marcos Orchard
Keyword(s):  
Electric Vehicles ◽  
Accurate Estimation ◽  
Just In Time ◽  
Computationally Efficient ◽  
Leibler Divergence ◽  
Key Factor ◽  
Discharge Model ◽  
Battery Energy ◽  
Computational Resources ◽  
Time Point

Battery energy systems are becoming increasingly popular in a variety of systems, such as electric vehicles. Accurate estimation of the total discharge of a battery is a key element for energy management. Problems such as path planning for drones or road choices in electric vehicles would benefit greatly knowing beforehand the end of discharge time. These tasks are generally performed online and require continuously quick estimations. We propose a novel prognostic method based on a combination of classic Riemann sampling (RS) and Lebesgue sampling (LS) applied to a discharge model of a battery. The method utilizes an early and inaccurate prediction using a RS-based method combined with a particle-filter based prognostic. Once a fault condition has been detected, subsequent Just-in-Time Point (JITP) estimations are updated using a novel LS-based method. The JITP prediction are triggered when the Kullback-Leibler divergence between the probability density functions (PDF) of the long-term-based prediction and the last filtered state reaches a threshold. The CPU time needed to execute a procedure is used as a measure of the computational resources. Results show that this combined approach is several orders of magnitude faster than the classical prognosis scheme. The combination of these two methods provides a robust JITP prognosis with less computational resources, a key factor to consider in real-time applications in embedded systems.

scholarly journals Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

2021 ◽  
Vol 4 ◽  
Author(s):  
Benjamin Hawks ◽  
Javier Duarte ◽  
Nicholas J. Fraser ◽  
Alessandro Pappalardo ◽  
Nhan Tran ◽  
...  
Keyword(s):  
Neural Networks ◽  
Information Content ◽  
Network Inference ◽  
High Energy Physics ◽  
High Energy ◽  
Bayesian Optimization ◽  
Low Latency ◽  
Computationally Efficient ◽  
Potential Applications ◽  
Efficient Machine

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.

scholarly journals MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

2021 ◽  
Author(s):  
Susitra Dhanarajalu
Keyword(s):  
Regression Analysis ◽  
Fuzzy Inference ◽  
Simulated Data ◽  
Switched Reluctance Motor ◽  
Simulation Software ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Non Linear

This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.

scholarly journals Modeling and Experimental Validation of the Chaotic Behavior of a Robot Whip

2019 ◽  
Vol 36 (3) ◽  
pp. 373-394
Author(s):  
Thomas M. Kwok ◽  
Zheng Li ◽  
Ruxu Du ◽  
Guanrong Chen
Keyword(s):  
Degrees Of Freedom ◽  
Experimental Validation ◽  
Chaotic Behavior ◽  
Experimental Testing ◽  
Modern Technology ◽  
Soft Body ◽  
Revolute Joints ◽  
Air Damping ◽  
Rigid Body Model ◽  
Potential Applications

ABSTRACTAlthough the whip is a common tool that has been used for thousands of years, there have been very few studies on its dynamic behavior. With the advance of modern technology, designing and building soft- body robot whips has become feasible. This paper presents a study on the modeling and experimental testing of a robot whip. The robot whip is modeled using a Pseudo-Rigid-Body Model (PRBM). The PRBM consists of a number of pseudo-rigid-links and pseudo-revolute-joints just like a multi-linkage pendulum. Because of its large number of degrees of freedom (DOF) and inherited underactuation, the robot whip exhibits prominent transient chaotic behavior. In particular, depending on the initial driving force, the chaos may start sooner or later, but will die down because of the gravity and air damping. The dynamic model is validated by experiments. It is interesting to note that with the same amount of force, the robot whip can generate a velocity more than 3 times and an acceleration up to 43 times faster than that of its rigid counterpart. This gives the robot whip some potential applications, such as whipping, wrapping and grabbing. This study also helps to develop other soft-body robots that involve nonlinear dynamics.

Influence of Inelastic Scattering at Metal-Dielectric Interfaces

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Patrick E. Hopkins ◽  
Pamela M. Norris ◽  
Robert J. Stevens
Keyword(s):  
Inelastic Scattering ◽  
High Temperatures ◽  
Experimental Testing ◽  
Phonon Transport ◽  
Dynamic Simulations ◽  
Interfacial Transport ◽  
Thermal Boundary Conductance ◽  
Dielectric Interfaces ◽  
Transport Channels

Thermal boundary conductance is becoming increasingly important in microelectronic device design and thermal management. Although there has been much success in predicting and modeling thermal boundary conductance at low temperatures, the current models applied at temperatures more common in device operation are not adequate due to our current limited understanding of phonon transport channels. In this study, the scattering processes across Cr∕Si, Al∕Al2O3, Pt∕Al2O3, and Pt∕AlN interfaces were examined by transient thermoreflectance testing at high temperatures. At high temperatures, traditional models predict the thermal boundary conductance to be relatively constant in these systems due to assumptions about phonon elastic scattering. Experiments, however, show an increase in the conductance indicating inelastic phonon processes. Previous molecular dynamic simulations of simple interfaces indicate the presence of inelastic scattering, which increases interfacial transport linearly with temperature. The trends predicted computationally are similar to those found during experimental testing, exposing the role of multiple-phonon processes in thermal boundary conductance at high temperatures.

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