automatic selection of ergonomic indicators for the design...

Automatic selection of ergonomic indicators for the design ofcollaborative robots: a virtual-human in the loop approach

P. Maurice, P. Schlehuber, Y. Measson, V. Padois, P. Bidaud

F R O M R E S E A R C H T O I N D U S T R Y

2014 IEEE-RAS International Conference on Humanoid RobotsNovember 19, 2014

Introduction

Work-related musculoskeletal disorders: A major health problem

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 1 / 11

Statistics [Schneider and Irastorza, 2010]

I Affect over 35 % of workers in EuropeI Represent the 1st occupational diseaseI Increase by 15 % per yearI Cost about $50B a year in the US

Carpal tunnel syndrome

Rotator cuff tendinitis

Bursitis

Epicondylitis

Achilles tendinitis

Low back pain

Tension necksyndrome

Main biomechanical risk factorsI Extreme posturesI Considerable effortsI Static workI High frequency of the gestures

Introduction

Collaborative robotics: A physical assistance for complex tasks


Robot [Colgate et al., 2003]

I Weight compensationI Strength amplificationI Guidance via virtual paths

HumanI Technical expertiseI AdaptabilityI Decision

Co-manipulation of objects or tools

Introduction

Limitations of DHM based ergonomic assessments


Macroscopic modelsI Products: Delmia, Jack 1,

Sammie 1, 3DSSPP 2 . . .I Ergonomic assessments:

RULA 5, OWAS 5, Snook tables 6,NIOSH 7, Low-back analysis . . .

Rough or Task-specificOne global criterion

Biomechanical modelsI Products: OpenSim 3, Anybody 4,

LifeMOD, Santos . . .I Ergonomic assessments:

Joint force, Muscle force, Tendonlength . . .

Numerous criteriaAccurate and Generic

1[Delleman et al., 2004], 2[Chaffin et al., 2006], 3[Delp et al., 2007], 4[Damsgaard et al., 2006],5[Li and Buckle, 1999], 6[Snook and Ciriello, 1991], 7[Waters et al., 1993]

Introduction

Limitations of DHM based ergonomic assessments


Macroscopic modelsI Products: Delmia, Jack 1,

Sammie 1, 3DSSPP 2 . . .I Ergonomic assessments:

RULA 5, OWAS 5, Snook tables 6,NIOSH 7, Low-back analysis . . .

Rough or Task-specificOne global criterion

Biomechanical modelsI Products: OpenSim 3, Anybody 4,

LifeMOD, Santos . . .I Ergonomic assessments:

Joint force, Muscle force, Tendonlength . . .

Numerous criteriaAccurate and Generic

1[Delleman et al., 2004], 2[Chaffin et al., 2006], 3[Delp et al., 2007], 4[Damsgaard et al., 2006],5[Li and Buckle, 1999], 6[Snook and Ciriello, 1991], 7[Waters et al., 1993]

Selection of relevant ergonomic indicatorsI Dedicated to the comparison of

collaborative robotsI Dependent on task featuresI Independent from robot designI Automatic DHM-based process

One global criterion Accurate and Generic

Method


1 Introduction

2 Method

3 Results

4 Conclusion

Method

Indicators relevance: Differentiating various ways of performing a task


Dynamic simulation

Task description

Robot controllerForce amplification

Manikin controller

LQP

Parameters set #NParameters set #1

Human and robotparameters

Selection

...

Analysis

Relevant ergonomic indicators

Indicators set #1 Indicators set #N...

Method

Parameters selection: Creating a variety of situations


MK

B

Frob = α Fvh

Frob

Collaborative robot Abstraction of the robot

τrob = α JT Fvh + g(q)

ParametersI Amplification coefficientI Robot massI Upper body joint limitsI Pelvis positionI Upper body reference postureI Upper body tasks weightsI Step lengthI Human sizeI Human body mass index

ExplorationFourier amplitude sensitivity

testing (FAST) [Saltelli et al., 1999]

Method

A dynamic DHM for indicators calculation


TasksI Balance: ZMP preview controlI Hands trajectories and forcesI Whole body postureI Torques minimization

Optimization [Salini et al., 2011]

Linear Quadratic Programmingwith weighting strategy

ConstraintsI Dynamical model equationI Joint limitsI Joint torques saturationI Non sliding contacts

Joint torquesContact forcesManikin stateaa

bb

{ }Ergonomicindicators

Dynamic simulationXDE framework (CEA-LIST)

”Robot” controllerForce amplification

”Torques”

Robot stateInteraction force

Method

Ergonomic indicators selection: A variance-based analysis


Indicator 1 Indicator N Local indicatorsI PositionsI VelocitiesI AccelerationsI TorquesI Power

I BackI Right armI Left armI Legs

Global indicatorsI Kinetic energyI Force transmission ratioI Velocity transmission ratioI Balance robustnessI Dynamic balance

∫task

I1(t) dt

Scaling

Variance

...

...

...

...

∫task

IN(t) dt

Scaling

Variance

Scree test: elbow criterion

Discriminating indicators

Method



Indicator 1 Indicator N

∫task

I1(t) dt

Scaling

Variance

...

...

...

...

∫task

IN(t) dt

Scaling

Variance



Scaling Scaling

Scaling valueI mean(Ii/I ref

i ) = 1I variance(Ii/I ref

i ) 6= 1

I refi =

∑m∈T

∑n∈P

Im,ni

NT NP

T : tasks, P: parameters sets

Method



Indicator 1 Indicator N

∫task

I1(t) dt

Scaling

Variance

...

...

...

...

∫task

IN(t) dt

Scaling

Variance



Scaling valueI mean(Ii/I ref

i ) = 1I variance(Ii/I ref

i ) 6= 1

I refi =

∑m∈T

∑n∈P

Im,ni

NT NP

T : tasks, P: parameters sets


Scree plot

I1 I2 I3 I4 I5 I7I6

Varia

nces

Indicators

elbow

selected not selected

Results


1 Introduction

2 Method

3 Results

4 Conclusion

Results

Application to various tasks: Selected indicators


Walk Reach Fast traj. Push Bend andsideways 35 cm tracking 100 N Carry 3 kg

81 % 80 % 86 % 81 % 82 %

Walk Reach Fast traj. Push Bend andsideways 35 cm tracking 100 N Carry 3 kg

Kinetic energyVelocity Transmission Ratio

Force Transmission RatioDynamic balance

Balance robustnessLegs torqueLegs power

Legs accelerationLegs velocityLegs position

Left arm torqueLeft arm power

Left arm accelerationLeft arm velocityLeft arm positionRight arm torqueRight arm power

Right arm accelerationRight arm velocityRight arm position

Back torqueBack power

Back accelerationBack velocityBack position

I 3 to 8 indicators selectedout of 29

I Variance information loss< 20 %

Results

Application to various tasks: Illustration of the parameters effects


Conclusion


1 Introduction

2 Method

3 Results

4 Conclusion

Conclusion

Conclusion: Automatic selection of ergonomic indicators


ConstraintsI Dedicated to collaborative

roboticsI Independent from robot designI Dependent on task featuresI AutomaticI Differentiate various ways of

performing a task

MethodI Parameters: Varying human and

robot (abstraction) featuresI Dynamic simulation: DHM with

LQP based controllerI Indicators: Variance-based

analysis of mutliple biomechanicalquantities

ResultsI Physically consistent selectionI 6 relevant indicators on averageI > 80 % information remains

Conclusion

Future work: Application to industrial jobs


Application to complex tasksI Indicator

∫task I(t) dt → Suitable for elementary tasks only

I Industrial tasks = Succession of elementary tasksI Where is the limit between 2 tasks?

subtask 1 subtask 2 subtask 3

influence influence

limit? limit?

Optimal design of collaborative robotsI Identify the most influential parameters to orient the design

work: Sensitivity analysis

I Combine assessment method with evolutionary algorithmI Optimize mechanical and/or control parameters of the robot

Conclusion


Thank you

Appendices

LQP Controller [Salini et al., 2011]

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014

Optimization

argminX

∑i

ωiTi(X) where X = (τ , wc, q)T

s.t.{

M(q)q + C(q, q) + g(q) = S τ − JTc (q)wc

GX � h

Tasks

• Joint torque ‖τ − τ ∗‖2

• Operational space wrench ‖wi − w∗i ‖2

• Joint acceleration ‖q− q∗‖2

• Operational space acceleration ‖Ji q + Ji q− X∗i ‖2

with X∗ = Xgoal + Kv (Xgoal − X) + Kp(Xgoal − X)

automatic selection of ergonomic indicators for the design...

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