1 DTSI /COGNITICS, ROBOTICS AND INTERACTION UNIT Virtual Human ( review )

1DTSI /COGNITICS, ROBOTICS AND INTERACTION UNIT

Virtual Human

( review )


Plan

Need

Human motion : influential factors Proof

Control scheme (functional architecture) Pb of stability Details

Test cases relevance

Reusability

Progress


Need

VH in mock-up

Autonomous manikin

Interactive manikin

Other techniques

Autonomy

(nb of ddl automatically driven)

Nb ddl ctrl > nb ddl cde :

- automate : nb ddl cde automation

- take time : drive ddl one after another

- standard devices

mouse, trackball…

- PDM auto check

- persuasive

- heavy infrastructure

- not relevant

uses

[Ren04]


Need

VH in mock-up

Autonomous manikin

Interactive manikin

Other techniques

Autonomy

(nb of ddl automatically driven)

- standard devices

mouse, trackball…

- PDM auto check

- persuasive

- heavy infrastructure

- not relevant

uses

Virtual cockpit

Helicopter maintenance

Test cases : Need :

- visibility- reaching- retarget control- balance control- use tools (grasping)- accessibility- collision- interaction (env.)- strength analysis- energy expenditure

[Ren04]

[Ren04]


Plan

Need




Reusability

Progress


Human motion : influential factors

Motion sources :

- reaching goals : effectors control

external (cartesian) potentials

Rem : concurrent tasks

tasks priority (decoupling)

- physical, biological constraints contact (collision / interaction)

joint limits

balance

unilateral constraints

- humans specificity attitude

energy expenditure control

internal (articular) potentials



Motion sources :






joint limits

balance






Decoupling : why?

- effector’s position controlled

- internal potential not considered yet

- effector’s position controlled

- internal potential optimized without decoupling

First step Second step



Motion sources :






joint limits

balance






Unilateral constraints : why?

joint limits : contact :

balance :

ex : retargeting of a giant’s mvt to a baby’

skeleton



Motion sources :






joint limits

balance






Attitude : why?

3DSmax 7’s IK : no "attitude" management


Plan

Need




Reusability

Progress


The system to be controlled

The manikin is seen as deformable skin which is controlled thanks to a skeleton.

Skeleton: polyarticulated arborescent kinematical chain

The skeleton is given a geometrical representation to allow collision tests

with environment

the lowest control-level is done through actuated joints


Global control scheme

Control

Simulation

6D targets positions and

velocitiesavatar joint positions

rigid bodies positions

avatar joint torques virtual human

being animated in real time

real world actor’s movements

being observed through motion

capture


Detailed control scheme (functional view)In

problems solved jointly

Robotic framework :

GVM (CEA) : simple control,

contact, limits…

Decoupling :- n targets priority

Method : dynamics

Attitude :- realism

Method : potential functions optimisation

Balance :- enforce mvt feasibility

Method : unilateral constraint (LCP)

Legend : : information flow

: I \ O ports

1st order dynamics

Interaction forces

dynamics

API : integrator

API : ulc solver

Out (visu 3D)

Projections :

- help (virtual guides)

- first step towards autonomy

passively projected6D marker

pos

manikin’ state

compensation torquetorque

contribution at each control

point

projected torque

contributions

manikin’s articular

configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Collision detection (searches Local Minimal Distance) : LMD++ (CEA)


Key points : stability by passivity

Controller >0

RT simulation >0

+

-

Input device:MocapHaptic

Etc.

Both blocks are discrete and non linear

The controller & the Real Time simulation should be passive to ensure stability

passivity :

integration scheme

passivity :

each control block

Passivity: control paradigm ensuring stability. Useful in complex systems, because it allows to decouple the passivity (thus stability) of the whole system into the passivity of each of the components of the system





Method : dynamics

Attitude :- realism





: I \ O ports

API : integrator

API : ulc solver

Out (visu 3D)

Projections :




pos

manikin’ state



point

projected torque

contributions


configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Robotic framework :


contact, limits…

1st order dynamics

Interaction forces

dynamics



First order dynamics & control (1/2)

usual 2nd order dynamics:

passage en dynamique du 1er ordre mass is neglected joints are separated into position controlled joints, and force

controlled joints

integration is done through additional joint damping

qGqqqCqqM ,

p

f

q

qq

fartqB

fartqB

couples articulaires

NOTE : First order dynamics and external control are handled by GVM (product developped by CEA\LIST)


PD cartesian control has a component used for internal control, and a

component which is a PD corrector to achieve external control

developping this expression we obtain the evolution equation in joint space

in previous equation, the inverse can be calculated under given conditions (not restrictive)

First order dynamics & control (1/2)

fC

farti

iidiiidiTiff qBvvBxxKJC

fi

iidiipipiTif

iifi

Tifartf CxxKvqJBJJBJBq

1

system’s jacobien control gains

NOTE : First order dynamics and external control are handled by GVM (product developped by CEA\LIST)





Method : dynamics

Attitude :- realism





: I \ O ports

API : integrator

API : ulc solver

Out (visu 3D)

Projections :




pos

manikin’ state



point

projected torque

contributions


configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Robotic framework :


contact, limits…

1st order dynamics

Interaction forces

dynamics



Decoupling (prioritization) (1/3)

State of the art : kinematics : multi-tasks dynamics :

• 2 tasks passive

• multi-tasks non passive

Innovation : 1st order dynamics multi-tasks passive decoupling (GVM) 2nd order dynamics multi-tasks passive decoupling

Tested approaches : multi-tasks kinematics : (OK), no interaction dismissed

Done, to do : internal potential ok, external potentials ? implement algorithm, test


Problem

Mathematical view (without prioritization)


without prioritization:

in case of target conflicts no target is reached

with prioritization:

in case of target conflicts, targets priorities are enforced

21

task 1task 2 task 2

task 1

influence (torque) of task 2 can disturb task 1


Solution project task 2’s influence such that it does not disturb higher

priority tasks

this control can be extended to n tasks


211 projection to avoid disturbing task 1


Control scheme (functional p.o.v.)In



Method : dynamics

Attitude :- realism





: I \ O ports

API : integrator

API : ulc solver

Out (visu 3D)

Projections :




pos

manikin’ state



point

projected torque

contributions


configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Robotic framework :


contact, limits…

1st order dynamics

Interaction forces

dynamics



Attitude (1/3)

Principal : optimize potential function caracterizing a "human attitude"

State of the art : potential functions empirical, not generic…

Innovation : ?

Done, to do : implement, and test

Test : ideally : compare positions of limbs (VH / n performers)

• difficult, interesting ? : empirically (human brain outstanding!)


Problem because of its redundant nature,a virtual human is still under-

constrained after solving for the cartesian constraints thus several configurations allow to enforce cartesian constraints solving for the internal problem (or attitude) allows to choose the

configuration that best represent a human posture

Mathematical view solving for cartesian control (dimensional problem) :

the relation J is not invertible : the system has an infinite number of solutions (such systems are called redundant)

Attitude (2/3)

=q

Jx

joint parameterseffector’s position


Solution find one solution q1 to the cartesian control (thanks to the moore-

penrose inverse) :

add constraints to the systems (other tasks), to fully constrain the problem

Attitude (3/3)

11

min qJxq

21 qqq

cartesian tasks contributioninternal tasks (attitude) contribution (must

not disturb cartesian control)

total joint parameters increment at current time-step





Method : dynamics

Attitude :- realism





: I \ O ports

API : integrator

API : ulc solver

Out (visu 3D)

Projections :




pos

manikin’ state



point

projected torque

contributions


configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Robotic framework :


contact, limits…

1st order dynamics

Interaction forces

dynamics



Balance (1/3)

State of the art : static equilibrium of a posture caracterized

• critical for test cases

Innovation : new approach (well posed) suits the global VH control scheme

Numerical methods : unilateral constraint thanks to a LCP solver (Lemke)

Done, to do : being implemented, and tested

COM


Problem

Mathematical view

Balance (2/3)

real "giant" actor

virtual "dwarf" avatar

targets constrained(desired position

specified by actor)

when movements of a "giant" actor are retargeted on a "dwarf" avatar, the dwarf may

not be balanced anymore

this leads to unfeasible movements of the avatar

k

kkcom

f

zpfzx

force at contact k

lever arm of contact kCOM’s position

vertical vector

Interpretation:

the projection of the COM on the ground must lie in the "support polygon"


Solution

Limitation the mathematical view shown here is rather restrictive (only

handles a sub-set of the problem) : contacts must lie on the same plane

Balance (3/3)

both feetactual support polygon

elliptical approximation of

the support polygon

projection of the COM on the ground

approximation of the support polygon by an ellipse

Enforce

22dxxP

Qccom (which states that the projection of the COM on

the ground must lie in the "support polygon")

while solving the evolution equation.

NOTE : we solved the problem on an elliptical approximation of the support polygon, it could be easily extended to other shapes…





Method : dynamics

Attitude :- realism





: I \ O ports

API : integrator

API : ulc solver

Out (visu 3D)

Projections :




pos

manikin’ state



point

projected torque

contributions


configuration

attitude torque’s

contribution

6Dpos

Mo

tio

n c

ap

ture

Robotic framework :


contact, limits…

1st order dynamics

Interaction forces

dynamics



Projections : semi-autonomous mode (1/3)

Principal : guide the manikin through projections

State of the art : matrix projections «as is» : passivity intrinsically passive projections through virtual mechanisms

Innovation : new to virtual humans control

Done, to do : implemented, tested, being published

Test : Virtual human performing a precision task


Problem because of the lack of haptic sensations, it can be difficult for the

the immersed actor to achieve some movements the most intuitive solution to implement these guides is to project

targets’ positions : but projections are unsafe as for passivity

Solution build passive projections thanks to mechanical analogies also

called virtual mechanisms (real world mechanisms being always passive, we can emulate them)



Solution


virtual mechanism:

guides movements

wall to be drilled

constraint coupling

target, and task space control

control point

future hole position

virtual human holding a drill

virtual mechanism (in red) helping the manikin aligning a drill on the futur hole’s position


Plan

Need




Reusability

Progress


Test cases relevance (1 / 2)

Virtual cockpit test

Highlighted points :

- multi-targets decoupling

- attitude


Method : dynamics

Attitude :- realism



configuration

attitude torque’s

contribution


Test cases relevance (2 / 2)

Helicopter (ladder climbing) test



Highlighted points :

- multi-targets decoupling

- attitude

- balance control


Method : dynamics

Attitude :- realism



configuration

attitude torque’s

contribution


Plan

Need




Reusability

Progress


Reusability

work direction driven by test cases

useful work… work

test cases

Code (functions mapping)

Methods

Architecture

Specifications (thesis)


Plan

Need




Reusability

Progress


Progress report

Interactive manikin :

Decoupling :

Balance :

Attitude :

Test cases :

Thesis editing :

Todaytook for granted to be done

publication Pacific Graphics

(fin avril 05)

Legend : : implementation

: theory

: tests

: publication

trainee

publication Virtual Concept

(fin mars 05)publication IROS

(début février 05)

publication SCSC

(fin février 05)

publication SCA

(mi avril 05)

Reviews :

avec IRCCyN

début mai 05:

Démonstrateurs niveau indus. pour Bourget?

avec IRCCyN

fin juin 05:

préparation rédaction thèse


Demo

1 DTSI /COGNITICS, ROBOTICS AND INTERACTION UNIT Virtual Human ( review )

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