5,736 research outputs found
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Real-life control tasks involve matters of various substances---rigid or soft
bodies, liquid, gas---each with distinct physical behaviors. This poses
challenges to traditional rigid-body physics engines. Particle-based simulators
have been developed to model the dynamics of these complex scenes; however,
relying on approximation techniques, their simulation often deviates from
real-world physics, especially in the long term. In this paper, we propose to
learn a particle-based simulator for complex control tasks. Combining learning
with particle-based systems brings in two major benefits: first, the learned
simulator, just like other particle-based systems, acts widely on objects of
different materials; second, the particle-based representation poses strong
inductive bias for learning: particles of the same type have the same dynamics
within. This enables the model to quickly adapt to new environments of unknown
dynamics within a few observations. We demonstrate robots achieving complex
manipulation tasks using the learned simulator, such as manipulating fluids and
deformable foam, with experiments both in simulation and in the real world. Our
study helps lay the foundation for robot learning of dynamic scenes with
particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video:
https://www.youtube.com/watch?v=FrPpP7aW3L
VISIO-HAPTIC DEFORMABLE MODEL FOR HAPTIC DOMINANT PALPATION SIMULATOR
Vision and haptic are two most important modalities in a medical simulation. While
visual cues assist one to see his actions when performing a medical procedure, haptic
cues enable feeling the object being manipulated during the interaction. Despite their
importance in a computer simulation, the combination of both modalities has not been
adequately assessed, especially that in a haptic dominant environment. Thus, resulting
in poor emphasis in resource allocation management in terms of effort spent in
rendering the two modalities for simulators with realistic real-time interactions.
Addressing this problem requires an investigation on whether a single modality
(haptic) or a combination of both visual and haptic could be better for learning skills
in a haptic dominant environment such as in a palpation simulator. However, before
such an investigation could take place one main technical implementation issue in
visio-haptic rendering needs to be addresse
Molecular transport and flow past hard and soft surfaces: Computer simulation of model systems
The properties of polymer liquids on hard and soft substrates are
investigated by molecular dynamics simulation of a coarse-grained bead-spring
model and dynamic single-chain-in-mean-field (SCMF) simulations of a soft,
coarse-grained polymer model. Hard, corrugated substrates are modelled by an
FCC Lennard-Jones solid while polymer brushes are investigated as a
prototypical example of a soft, deformable surface. From the molecular
simulation we extract the coarse-grained parameters that characterise the
equilibrium and flow properties of the liquid in contact with the substrate:
the surface and interface tensions, and the parameters of the hydrodynamic
boundary condition. The so-determined parameters enter a continuum description
like the Stokes equation or the lubrication approximation.Comment: 41 pages, 13 figure
Modeling, Simulation and Prediction of Vehicle Crashworthiness in Full Frontal Impact
Vehicle crashworthiness assessment is critical to help reduce road accident fatalities and ensure safer vehicles for road users. Techniques to assess crashworthiness include physical tests and mathematical modeling and simulation of crash events, the latter is preferred as mathematical modeling is generally cheaper to perform in comparison with physical testing. The most common mathematical modeling technique used for crashworthiness assessment is nonlinear Finite Element (FE) modeling. However, a problem with the use of Finite Element Model (FEM) for crashworthiness assessment is inaccessibility to individual researchers, public bodies, small universities and engineering companies due to need for detailed CAD data, software licence costs along with high computational demands. This thesis investigates modeling strategies which are affordable, computationally and labour inexpensive, and could be used by the above-mentioned groups. Use of Lumped Parameter Models (LPM) capable of capturing vehicle parameters contributing to vehicle crashworthiness has been proposed as an alternative to adopting FEM, while the later have been used to validate LPMs developed in this thesis.
The main crash scenario analysed is a full frontal impact against a rigid barrier. Front-end deformation which can be used to measure crash energy absorption and pitching which could lead to occupant injuries in a frontal crash event are parameters focused on. The thesis investigates two types of vehicles; vehicle with initial structure intact is defined as baseline vehicle, while a vehicle that underwent unprofessional repairs on its structural members made of Ultra High Strength Steel (UHSS) is defined as a modified vehicle.
The proposed novel LPM for a baseline vehicle impact is inspired by pendulum motion and expresses the system using Lagrangian formulation to predict the two phases of impact: front-end deformation and vehicle pitching.
Changes in crashworthiness performance of a modified vehicle were investigated with a FEM; tensile tests on UHSS coupons were conducted to generate material inputs for this FEM. Further, a full scale crash test was conducted to validate the FE simulations. An LPM to conduct crashworthiness assessment of a modified vehicle has been proposed, it is based on a double pendulum with a torsional spring representing the vehicle undergoing a full frontal impact.publishedVersio
Efficient techniques for soft tissue modeling and simulation
Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element
Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements
i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter
identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input
space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and
applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The
deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e.
triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an
opportunity for various deformation applications.
We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated
from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a
surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both
methods produce plausible results verified by surgeons
Soft volume simulation using a deformable surface model
The aim of the research is to contribute to the modelling of deformable objects, such as soft tissues in medical simulation. Interactive simulation for medical training is a concept undergoing rapid growth as the underlying technologies support the increasingly more realstic and functional training environments. The prominent issues in the deployment of such environments centre on a fine balance between the accuracy of the deformable model and real-time interactivity. Acknowledging the importance of interacting with non-rigid materials such as the palpation of a breast for breast assessment, this thesis has explored the physics-based modelling techniques for both volume and surface approach. This thesis identified that the surface approach based on the mass spring system (MSS) has the benefits of rapid prototyping, reduced mesh complexity, computational efficiency and the support for large material deformation compared to the continuum approach. However, accuracy relative to real material properties is often over looked in the configuration of the resulting model.
This thesis has investigated the potential and the feasibility of surface modelling for simulating soft objects regardless of the design of the mesh topology and the non-existence of internal volume discretisation. The assumptions of the material parameters such as elasticity, homogeneity and incompressibility allow a reduced set of material values to be implemented in order to establish the association with the surface configuration. A framework for a deformable surface model was generated in accordance with the issues of the estimation of properties and volume behaviour corresponding to the material parameters. The novel extension to the surface MSS enables the tensile properties of the material to be integrated into an enhanced configuration despite its lack of volume information. The benefits of the reduced complexity of a surface model are now correlated with the improved accuracy in the estimation of properties and volume behaviour. Despite the irregularity of the underlying mesh topology and the absence of volume, the model reflected the original material values and preserved volume with minimal deviations. Global deformation effect which is essential to emulate the run time behaviour of a real soft material upon interaction, such as the palpation of a generic breast, was also demonstrated, thus indicating the potential of this novel technique in the application of soft tissue simulation
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