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Automatic selection of ergonomic indicators for the design of collaborative 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 Robots November 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]

◮ Affect over 35 % of workers in Europe ◮ Represent the 1st occupational disease ◮ Increase by 15 % per year ◮ Cost about $50B a year in the US

Carpal tunnel syndrome Rotator cuff tendinitis Bursitis Epicondylitis Achilles tendinitis Low back pain Tension neck syndrome

Main biomechanical risk factors

◮ Extreme postures ◮ Considerable efforts ◮ Static work ◮ High frequency of the gestures

Introduction

Collaborative robotics: A physical assistance for complex tasks

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Robot [Colgate et al., 2003]

◮ Weight compensation ◮ Strength amplification ◮ Guidance via virtual paths

Human

◮ Technical expertise ◮ Adaptability ◮ Decision

Co-manipulation of objects or tools

Introduction

Limitations of DHM based ergonomic assessments

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 3 / 11

Macroscopic models

◮ Products:

Delmia, Jack 1, Sammie 1, 3DSSPP 2 . . .

◮ Ergonomic assessments:

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

Rough or Task-specific One global criterion Biomechanical models

◮ Products:

OpenSim 3, Anybody 4, LifeMOD, Santos . . .

◮ Ergonomic assessments:

Joint force, Muscle force, Tendon length . . .

Numerous criteria Accurate 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

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 3 / 11

Macroscopic models

◮ Products:

Delmia, Jack 1, Sammie 1, 3DSSPP 2 . . .

◮ Ergonomic assessments:

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

Rough or Task-specific One global criterion Biomechanical models

◮ Products:

OpenSim 3, Anybody 4, LifeMOD, Santos . . .

◮ Ergonomic assessments:

Joint force, Muscle force, Tendon length . . .

Numerous criteria Accurate 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 indicators

◮ Dedicated to the comparison of

collaborative robots

◮ Dependent on task features ◮ Independent from robot design ◮ Automatic DHM-based process

One global criterion Accurate and Generic

Method Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 3 / 11

1

Introduction

2

Method

3

Results

4

Conclusion

Method

Indicators relevance: Differentiating various ways of performing a task

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 4 / 11

Dynamic simulation Task description Robot controller

Force amplification

Manikin controller

LQP

Parameters set #N Parameters set #1 Human and robot parameters Selection ... Analysis Relevant ergonomic indicators Indicators set #1 Indicators set #N ...

Method

Parameters selection: Creating a variety of situations

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 5 / 11

M K B

Frob = α Fvh

Frob

Collaborative robot Abstraction of the robot

τrob = α JT Fvh + g(q)

Parameters

◮ Amplification coefficient ◮ Robot mass ◮ Upper body joint limits ◮ Pelvis position ◮ Upper body reference posture ◮ Upper body tasks weights ◮ Step length ◮ Human size ◮ Human body mass index

Exploration Fourier amplitude sensitivity testing (FAST) [Saltelli et al., 1999]

Method

A dynamic DHM for indicators calculation

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 6 / 11

Tasks

◮ Balance: ZMP preview control ◮ Hands trajectories and forces ◮ Whole body posture ◮ Torques minimization

Optimization [Salini et al., 2011]

Linear Quadratic Programming with weighting strategy

Constraints

◮ Dynamical model equation ◮ Joint limits ◮ Joint torques saturation ◮ Non sliding contacts

Joint torques Contact forces Manikin state aa bb

• Ergonomic

indicators Dynamic simulation

XDE framework (CEA-LIST)

”Robot” controller

Force amplification

”Torques” Robot state Interaction force

Method

Ergonomic indicators selection: A variance-based analysis

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 7 / 11

Indicator 1 Indicator N Local indicators

◮ Positions ◮ Velocities ◮ Accelerations ◮ Torques ◮ Power

                     

◮ Back ◮ Right arm ◮ Left arm ◮ Legs

Global indicators

◮ Kinetic energy ◮ Force transmission ratio ◮ Velocity transmission ratio ◮ Balance robustness ◮ Dynamic balance

• task

I1(t) dt Scaling Variance ... ... ... ...

• task

IN(t) dt Scaling Variance Scree test: elbow criterion Discriminating indicators

Method

Ergonomic indicators selection: A variance-based analysis

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 7 / 11

Indicator 1 Indicator N

• task

I1(t) dt Scaling Variance ... ... ... ...

• task

IN(t) dt Scaling Variance Scree test: elbow criterion Discriminating indicators Scaling Scaling Scaling value

◮ mean(Ii/Iref i

) = 1

◮ variance(Ii/Iref i

) = 1 Iref

i

=

• m∈T
• n∈P

Im,n

i

NT NP

T: tasks, P: parameters sets

Method

Ergonomic indicators selection: A variance-based analysis

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 7 / 11

Indicator 1 Indicator N

• task

I1(t) dt Scaling Variance ... ... ... ...

• task

IN(t) dt Scaling Variance Scree test: elbow criterion Discriminating indicators Scaling value

◮ mean(Ii/Iref i

) = 1

◮ variance(Ii/Iref i

) = 1 Iref

i

=

• m∈T
• n∈P

Im,n

i

NT NP

T: tasks, P: parameters sets

Scree test: elbow criterion Scree plot

I1 I2 I3 I4 I5 I7 I6

Variances Indicators elbow selected not selected

Results Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 7 / 11

1

Introduction

2

Method

3

Results

4

Conclusion

Results

Application to various tasks: Selected indicators

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 8 / 11

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

81 % 80 % 86 % 81 % 82 %

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

Kinetic energy Velocity Transmission Ratio Force Transmission Ratio Dynamic balance Balance robustness Legs torque Legs power Legs acceleration Legs velocity Legs position Left arm torque Left arm power Left arm acceleration Left arm velocity Left arm position Right arm torque Right arm power Right arm acceleration Right arm velocity Right arm position Back torque Back power Back acceleration Back velocity Back position ◮ 3 to 8 indicators selected

• ut of 29

◮ Variance information loss

< 20 %

Results

Application to various tasks: Illustration of the parameters effects

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 9 / 11

Conclusion Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 9 / 11

1

Introduction

2

Method

3

Results

4

Conclusion

Conclusion

Conclusion: Automatic selection of ergonomic indicators

Pauline MAURICE Selection of ergonomic indicators for collaborative robotics using a DHM Humanoids 2014 10 / 11

Constraints

◮ Dedicated to collaborative

robotics

◮ Independent from robot design ◮ Dependent on task features ◮ Automatic ◮ Differentiate various ways of

performing a task Method

◮ Parameters: Varying human and

robot (abstraction) features

◮ Dynamic simulation: DHM with

LQP based controller

◮ Indicators: Variance-based

analysis of mutliple biomechanical quantities Results

◮ Physically consistent selection ◮ 6 relevant indicators on average ◮ > 80 % information remains

Conclusion

Future work: Application to industrial jobs

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Application to complex tasks

◮ Indicator task I(t) dt

→ Suitable for elementary tasks only

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

subtask 1 subtask 2 subtask 3 influence influence limit? limit? Optimal design of collaborative robots

◮ Identify the most influential parameters to orient the design

work: Sensitivity analysis

◮ Combine assessment method with evolutionary algorithm ◮ Optimize mechanical and/or control parameters of the robot

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

Thank you

Appendices

LQP Controller [Salini et al., 2011]

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

Optimization argmin

X

• i

ωiTi(X) where X = (τ, wc, ¨ q)T s.t.

• M(q)¨

q + C(q, ˙ q) + g(q) = S τ − JT

c (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)