Renderingpeople.rennes.inria.fr/Anatole.Lecuyer/harders.pdf · 2011. 10. 27. · 2 March 9th 2008 Matthias Harders / Computer Vision Laboratory / [email protected] Process

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March 9th 2008

Data-Driven Haptic Rendering

Matthias Harders, Raphael HoeverVirtual Reality in Medicine GroupComputer Vision Lab, ETH Zurich

2March 9th 2008 Matthias Harders / Computer Vision Laboratory / [email protected]

Analogy – Computer Graphics

Conventional CG rendering

Rendering


Analogy – Computer Graphics

Image based rendering

Rendering


Process Overview

Record data from real object

Process data

Synthesize feedbackin virtual environment


Key Concepts

Haptic signals acquired during user interaction

No physical models employed

Forces synthesized based on recordings


(Brief) Related Work

Kuchenbecker et al., Event-based haptics

Mahvash, Hayward, Tool-tissue contact

Pai et al., ACME, WHaT, Interaction cap. & syn.

MacLean, Haptic camera

Colton, Hollerbach, Switch force playback

…

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Process Overview


Process data



Recording Setup


Technical Specifications

Position sensing6 DOF, resolution 0.03 mmworkspace 381 x 267 x 191 mm3

Force sensing6 DOF, resolution 1.6 mN, range ±25 N, noise ±10 mN

Force display3 DOF, resolution 2.2 mN, range ±1.4 N (8.5 N short term)


Signal Acquisition Artifacts

Position and force sensing not synchronized

Haptic rendering quality reduced due to jitter

Time

Position samplesForce samples


Real-Time System

RTAI machine with real-time drivers (Comedi)

Positions acquired via Sensoray DAQ board

Forces acquired via National Instruments board

Sampling frequencies up to 5 kHz

Sensoray DAQModel 626

National InstrumentsPCI-6220 board


Acquisition Synchronization

Acquisition jitter reduction (from ~100µs to ~1µs)

No signal interpolation required

Flexible sampling rates

Time

Position samplesForce samples

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Material Samples

Box-shaped frame (200 x 80 x 28 mm3)

Solid silicone cuboid (SmoothOn Ecoflex 0030)

Rubber membrane

siliconerubber


Data Acquisition Strategy

Positions, forces measured directly

Velocities derived via central differences, smoothed with moving average filter (100 samples)

Forces at same position and velocity averaged

Data reduction by only considering forces changes > 50mN

Typical interaction, 1kHz sampling: 75% reduction


Data Acquisition Strategy

Poking at different frequencies for elasticity andviscous damping

Force steps for material relaxation


Process Overview


Process data



Data Preprocessing

Interaction trajectory not constrained

Sparse sampling of object

Resample interaction domain

Interpolation strategies requiredSimplex-based interpolationRadial basis functions

Selection of data dimensions


Simplex-Based Interpolation

Fast computation of domain tessellation

Limited to convex hull of data

Slow calculation of interpolated values

No implicit smoothing

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Simplex-Based Interpolation

Select resampling density according to datadimensionality, storage requirements, andcomputation time

Example for 1D indentationΔp=0.5mm, Δv=7-40mm/s100 position and 21 velocity samples provide sufficient results

Additional dilation steps20March 9th 2008 Matthias Harders / Computer Vision Laboratory / [email protected]

Radial Basis Functions

Interpolation in the whole domain

Faster computation of interpolated values

Requires solution of linear system

Always smooth interpolants



1D example

Interpolation condition

Orthogonality constraints

( )∑ ∑= =

+−=N

j

l

kkkjj gdwF

1 1)()( xxxx φ

NifF ii ,,1for )( …==x

ligwN

jij ,,1for 0)(1j

…==∑=

x



Linear equation system results

Ill-conditioned due to sparse sampling

Apply truncated singular value decomposition

Polyharmonic splines as basis functions

High computational effort solving large systems

⎟⎟⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛0f

dw

0TGGA


Process Overview


Process data



1D Experiment – Rubber

1002 samples (8324 before reduction)

Voxel grid 100x21 samples (0.21mm, 7mm/s)

Simplex interpolation 3sec, RBF 50sec

rubber

Simplexinterpolation

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Offline validation of interpolated results

Compare to force measurements of test-trajectories



1D Experiment – Silicone


Voxel grid 100x21 samples (0.16mm, 8mm/s)

Simplex interpolation 3sec, RBF 128sec

silicone




Same validation strategy

Replication of stickiness of silicone



Analysis of Rendering Error

Mean error silicone: 86mN (simpl.), 102 mN (RBF)

Mean error rubber: 26mN (simplex), 23mN (RBF)

JND=7%

max=11.9%JND=7%max=4.8%

Silicone Rubber● Simplex ● RBF



Contact point fixed, no slip, lateral deflection


Voxel grid 45x45x11x11 samples (pos_x,y0.53mm, 0.51mm, vel_x,y 40.3mm/s, 28.7mm/s)

Simplex interpolation 4h (mainly precalculation)

RBF interpolation 106min

rubber



Comparison of actual and synthesized forces

RBFinterpolation

rubber

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Contact point fixed, no slip, lateral deflection


Voxel grid 28x26x11x11 samples (pos_x,y0.49mm, 0.53mm, vel_x,y 19.7mm/s, 13.7mm/s)

Simplex interpolation 4h (mainly precalculation)

RBF interpolation 37min

silicone



Comparison of actual and synthesized forces

RBFinterpolation

silicone


Analysis of Rendering Error

Silicone Rubber

JND=7%

max=23.7%

JND=7%

max=13.8%

● Simplex ● RBF


Extension to Sliding Contact

Inspired by Dahl's friction model

Detect interaction point outside of convex hull

Displace interpolation field to new position


Extension to Sliding Contact

Example: sliding over rubber membrane


Conclusions

Framework for data-driven haptic rendering

Comparison of different interpolation techniques

Offline experiments with test-trajectories

Future work focuses on adapting data dimensions due to material properties

Examine further materials

Perform perceptional user studies

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Acknowledgements

This work was supported by the EU project Immersence IST-027141

Main work by Raphael Hoever

Presentation at Haptic Symposium

Tutorial support by EU Network ofExcellence INTUITION IST/NMP-507248-2

Documents

Renderingpeople.rennes.inria.fr/Anatole.Lecuyer/harders.pdf · 2011. 10. 27. · 2 March 9th 2008 Matthias Harders / Computer Vision Laboratory / [email protected] Process