29,242 research outputs found
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
A stable FSI algorithm for light rigid bodies in compressible flow
In this article we describe a stable partitioned algorithm that overcomes the
added mass instability arising in fluid-structure interactions of light rigid
bodies and inviscid compressible flow. The new algorithm is stable even for
bodies with zero mass and zero moments of inertia. The approach is based on a
local characteristic projection of the force on the rigid body and is a natural
extension of the recently developed algorithm for coupling compressible flow
and deformable bodies. Normal mode analysis is used to prove the stability of
the approximation for a one-dimensional model problem and numerical
computations confirm these results. In multiple space dimensions the approach
naturally reveals the form of the added mass tensors in the equations governing
the motion of the rigid body. These tensors, which depend on certain surface
integrals of the fluid impedance, couple the translational and angular
velocities of the body. Numerical results in two space dimensions, based on the
use of moving overlapping grids and adaptive mesh refinement, demonstrate the
behavior and efficacy of the new scheme. These results include the simulation
of the difficult problem of a shock impacting an ellipse of zero mass.Comment: 32 pages, 20 figure
Efficient Monte Carlo Methods for Cyclic Peptides
We present a new, biased Monte Carlo scheme for simulating complex, cyclic
peptides. Backbone atoms are equilibrated with a biased rebridging scheme, and
side-chain atoms are equilibrated with a look-ahead configurational bias Monte
Carlo. Parallel tempering is shown to be an important ingredient in the
construction of an efficient approach.Comment: LaTeX source, 10 EPS figures, to appear in Molecular Physic
Evaluation of a new implicit coupling algorithm for the partitioned fluid-structure interaction simulation of bileaflet mechanical heart valves
We present a newly developed Fluid-Structure Interaction coupling algorithm to simulate Bileaflet Mechanical Heart Valves dynamics in a partitioned way. The coupling iterations between the flow solver and the leaflet motion solver are accelerated by using the Jacobian with the derivatives of the pressure and viscous moments acting on the leaflets with respect to the leaflet acceleration. This Jacobian is used in the leaflet motion solver when new positions of the leaflets are computed during the coupling iterations. The Jacobian is numerically derived from the flow solver by applying leaflet perturbations. Instead of calculating this Jacobian every time step, the Jacobian is extrapolated from previous time steps and a recalculation of the Jacobian is only done when needed. The efficiency of our new algorithm is subsequently compared to existing algorithms which use fixed relaxation and dynamic Aitken Δ2 relaxation in the coupling iterations when the new positions of the leaflets are computed. Results show that dynamic Aitken Δ2 relaxation outperforms fixed relaxation. Moreover, during the opening phase of the valve, our new algorithm needs fewer subiterations per time step to achieve convergence than the method with Aitken Δ2 relaxation. Thus, our newly developed FSI coupling scheme outperforms the existing coupling schemes
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
Immersed boundary-finite element model of fluid-structure interaction in the aortic root
It has long been recognized that aortic root elasticity helps to ensure
efficient aortic valve closure, but our understanding of the functional
importance of the elasticity and geometry of the aortic root continues to
evolve as increasingly detailed in vivo imaging data become available. Herein,
we describe fluid-structure interaction models of the aortic root, including
the aortic valve leaflets, the sinuses of Valsalva, the aortic annulus, and the
sinotubular junction, that employ a version of Peskin's immersed boundary (IB)
method with a finite element (FE) description of the structural elasticity. We
develop both an idealized model of the root with three-fold symmetry of the
aortic sinuses and valve leaflets, and a more realistic model that accounts for
the differences in the sizes of the left, right, and noncoronary sinuses and
corresponding valve cusps. As in earlier work, we use fiber-based models of the
valve leaflets, but this study extends earlier IB models of the aortic root by
employing incompressible hyperelastic models of the mechanics of the sinuses
and ascending aorta using a constitutive law fit to experimental data from
human aortic root tissue. In vivo pressure loading is accounted for by a
backwards displacement method that determines the unloaded configurations of
the root models. Our models yield realistic cardiac output at physiological
pressures, with low transvalvular pressure differences during forward flow,
minimal regurgitation during valve closure, and realistic pressure loads when
the valve is closed during diastole. Further, results from high-resolution
computations demonstrate that IB models of the aortic valve are able to produce
essentially grid-converged dynamics at practical grid spacings for the
high-Reynolds number flows of the aortic root
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