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John Trapp

dblp:63/2319 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computer animation and physical simulation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation › multibody dynamics simulation
constrained dynamics
0.112005
Effective Constrained Dynamic Simulation Using Implicit Constraint Enforcement · ICRA 2005
Computer animation and physical simulation
constraint enforcement
0.112005
Effective Constrained Dynamic Simulation Using Implicit Constraint Enforcement · ICRA 2005

Methods — techniques the papers use, named apart from their topics

implicit constraint enforcement · 0.1baumgarte stabilization · 0.1
YearPublicationVenuePosition
2005 Effective Constrained Dynamic Simulation Using Implicit Constraint Enforcement
abstract
Stable and effective enforcement of hard constraints is one of the crucial components in controlling physics-based dynamic simulation systems. The conventional explicit Baumgarte constraint stabilization confines the time step to be within a stability limit and requires users to pick problem-dependent coefficients to achieve fast convergence or to prevent oscillations. The recently proposed post-stabilization method has shown a successful constraint drift reduction but it does not guarantee the physically correct behavior of motion and requires additional computational cost to decrease the constraint errors. This paper presents our new implicit constraint enforcement technique that is stable over large time steps and does not require problem dependent stabilization parameters. This new implicit constraint enforcement method uses the future time step to estimate the correct magnitude of the constraint forces, resulting in better stability over bigger time steps. More importantly, the proposed method generates physically conforming constraint forces while minimizing the constraint drifts, resulting in physically correct motion. Its asymptotic computational complexity is same as the explicit Baumgarte method. It can be easily integrated into various constrained dynamic systems including rigid body or deformable structure applications. This paper describes a formulation of implicit constraint enforcement and an accumulated constraint error and dynamic behavior analysis for comparison with existing methods.
Min Hong, Min-Hyung Choi, Sunhwa Jung, Samuel W. J. Welch, John Trapp
ICRA5