Acquisition of Elastic Models for Interactive Simulation

Abstract

We present method and implementation to acquire deformable models of elastic objects. The method is based on the Green's functions matrix representation of an elastic solid. In this paper, we present a robust estimation technique for this Green's functions matrix. Robustness is achieved by regularization and a fitting technique which we describe here in detail. The underlying data for estimation are acquired with a robotic measurement system. We describe the UBC Active Measurement System (ACME) as it relates to the deformable model acquisition. In particular, we characterize the ability of ACME to record the global deformation of an object based on our previously presented range-flow technique. We provide results for two elastic objects: a plush toy and a medical physical soft-tissue wrist model.

Casting Manipulation With Unknown String via Motion Generation, Actual Manipulation, and Parameter Estimation

“…A typical approach to achieve this is to construct an accurate physical model and identify its parameters. Lang et al modeled the deformation of a deformable object by a discrete Green's function matrix and estimated the model using a specialized facility (The Active Measurement Facility) [7]. Chen et al developed a friction measurement device to accurately depict contact between a garment and an object on a computer [8].…”

Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

“…Various methods that explore analytical modeling approaches for non-rigid objects in robotic environments are inspired by physics-based models, extensively studied in computer graphics (Nealen et al, 2006). These include continuous mesh models such as Euler-Bernoulli (EB) (Fugl et al, 2012), linear Finite Element Method (FEM) (Lang et al, 2002;Frank et al, 2014;Jia et al, 2014;Petit et al, 2015;Duenser et al, 2018) and nonlinear FEM (Leizea et al, 2017;Sengupta et al, 2020). Also, discrete mesh models such as linear Mass-Spring Systems (MSS) (Leizea et al, 2014) and non-linear MSS (Zaidi et al, 2017) are considered.…”