A material point method for thin shells with frictional contact

Guo, Qi; Han, Xuchen; Fu, Chuyuan; Gast, Theodore; Tamstorf, Rasmus; Teran, Joseph

doi:10.1145/3197517.3201346

Cited by 54 publications

(27 citation statements)

References 55 publications

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“…The material point method has been extensively developed from both a solid mechanics [9] and computer graphics [10] perspective. As a hybrid Eulerian-Langrangian method, MPM has demonstrated its versatility in simulating snow [11], [12], sand [13], [14], non-Newtonion fluids [15], cloth [16], [17], solid-fluid coupling [18], [19], rigid body coupling, and cutting [8]. [20] also proposed an adaptive MPM scheme to concentrate computation resources in the regions of interest.…”

Section: A Materials Point Methodsmentioning

confidence: 99%

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Liu

Spielberg

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

194

139

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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.

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Section: A Materials Point Methodsmentioning

confidence: 99%

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Liu

Spielberg

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

194

139

View full text Add to dashboard Cite

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“…Hyde et al [2020] provide a thorough discussion of the state of the art. Our approach utilizes the particle-based MPM [ de Vaucorbeil et al 2020;Sulsky et al 1994] PIC technique, largely due to its natural ability to handle self collision [Fei et al 2018[Fei et al , 2017Guo et al 2018;, topology change Wolper et al 2020Wolper et al , 2019, diverse materials [Daviet and Bertails-Descoubes 2016;Klár et al 2016;Ram et al 2015;Schreck and Wojtan 2020;Stomakhin et al 2013;Wang et al 2020c;Yue et al 2015] as well as implicit time stepping with elasticity Fei et al 2018;Stomakhin et al 2013;Wang et al 2020b]. We additionally use the APIC method [Fu et al 2017;Jiang et al 2015 for its conservation properties and beneficial suppression of noise.…”

Section: Related Workmentioning

confidence: 99%

A momentum-conserving implicit material point method for surface tension with contact angles and spatial gradients

et al. 2021

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“…One of the strengths of MPM is its ability to treat complex materials like sand, snow, or gel, where plasticity often plays an important role. Frictional contacts can also be modeled directly using plasticity [JGT17, GHF*18, DS19, HGG*19]. The use of plasticity models generally complicates implicit formulations.…”

Section: Related Workmentioning

confidence: 99%

Effective time step restrictions for explicit MPM simulation

Sun

Shinar

Schroeder

2020

Computer Graphics Forum

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Time steps for explicit MPM simulation in computer graphics are often selected by trial and error due to the challenges in automatically selecting stable time step sizes. Our time integration scheme uses time step restrictions that take into account forces, collisions, and even grid‐to‐particle transfers calculated near the end of the time step. We propose a novel set of time step restrictions that allow a time step to be selected that is stable, efficient to compute, and not too far from optimal. We derive the general solution for the sound speed in nonlinear isotropic hyperelastic materials, which we use to enforce the classical CFL time step restriction. We identify a single‐particle instability in explicit MPM integration and propose a corresponding time step restriction in the fluid case. We also propose a reflection‐based boundary condition for domain walls that supports separation and accurate Coulomb friction while preventing particles from penetrating the domain walls.

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A material point method for thin shells with frictional contact

Cited by 54 publications

References 55 publications

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

A momentum-conserving implicit material point method for surface tension with contact angles and spatial gradients

Effective time step restrictions for explicit MPM simulation

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