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Thursday,December 20,2018
End-point stiffness estimation ofthehumanarm inHuman-RobotInteraction
Supervisor:Prof.ssaElenaDeMomi
LuisaCucugliato872310luisa.cucugliato@mail.polimi.itCo-supervisor:JacopoBuzzi
Context
Human-RobotInteraction (HRI)
“When humans, robots andtheenvironment cometocontact witheach other andform atighty coupled dynamical system toaccomplish atask”
[A.Ajoudani, A.Zanchettin.S.Ivaldi.A.Albu-Shaffer etal.,“Progressandprospects oftheHuman-Robot Collaboration”, HAL,2016]
Luisa Cucugliato 2
Human-RobotInteraction
RESEARCHCHALLENGE: Enable natural andintuitive human-robot communication
Safety
Stability
Trasparency
3Luisa Cucugliato
Humandynamics control
CentralNervousSystem
Arm jointconfiguration
Muscleco-contraction
Humandynamicproprieties
Humans perform awidevariety ofmovements byadjusting themechanical impedanceparameters oftheir musculoskeletal system inmotion
DAMPING,INERTIA,STIFFNESS
4Luisa Cucugliato
Humandynamics control
CentralNervousSystem
Arm jointconfiguration
Muscleco-contraction
Humandynamicproprieties
Controller
Robotdynamicproprieties
Humans perform awidevariety ofmovements byadjusting themechanical impedanceparameters oftheir musculoskeletal system inmotion
STIFFNESS
DAMPING,INERTIA
4Luisa Cucugliato
Humanarm stiffness
• It is themain responsible ofmuscular-skeletal stability• It is thefirstelement modulated inneural-motor controlstrategies
Thearm stiffness is themuscles response toexternal perturbations fromequilibrium point
STIFFNESSMODULATION
Kinematic:armconfiguration variations
Dynamic:muscles co-contraction level variations
+
Humanarm second-order linearmodel:
k
b
mF
5Luisa Cucugliato
Humanarm stiffness
Geometrically described as anellipsoid:
• Orientation• Eccentricity• Size
Parameterschange wrt thestiffness modulation strategy
[N.Hogan,“Adaptive control ofmechanical impedance bycoactivation ofan- tagonist muscles,”IEEETRANSACTIONSONAUTOMATICCONTROL,1984]
Majorprincipal axis
Minorprincipal axis
x
y
SVD
Principal axes Axes direction
6Luisa Cucugliato
Thesis goal
Toestimate thehumanarm end-point stiffness asdescriptor ofhumanintentionduringphysicalHRI
Creation ofastiffnessacquisition set-up
Implentation ofastiffness
estimation method
Stiffness analysis formultiplepositionsandmuscle co-contraction
levels
1 2 3
7Luisa Cucugliato
Stiffness Investigation:measurment
End-point stiffness is acquired measuring thehumanarm restoring force as response torobotend-effector displacementduring aphysical HRI
8Luisa Cucugliato
Stiffness Investigation:methods
METHODSKnown displacement Frequency perturbation
• Known displacements onplane• Linearrelationbetween forceand
displacement
• Donot explain thewhole harmdynamics
[Mussa-Ivaldi andE.Bizzi,“Neural, mechanical, andgeometric factors subserving arm postureinhumans,”TheJournalofNeuroscience Vol.5,No.10,1985]
Luisa Cucugliato 9
Stiffness Investigation:methods
METHODSKnown displacement Frequency perturbation
• Known displacements onplane• Linearrelationbetween forceand
displacement
• Donot explain thewhole harmdynamics
• Stochastic displacement inspace• Frequency description ofhuman
arm characteristics
• Provide inertia,damping andstiffness
[E.Perreault, R.Kirsch, A.M.Acosta ,“Multiple-input, multiple-output system identification forcharacterization oflimb stiffness dynamics”,Biological Cybernetics, 1999]
Luisa Cucugliato 9
Stiffness Investigation:methods
METHODSKnown displacement Frequency perturbation
• Known displacements onplane• Linearrelationbetween forceand
displacement
• Donot explain thewhole harmdynamics
• Stochastic displacement inspace• Frequency description ofhuman
arm characteristics
• Provide inertia,damping andstiffness
Luisa Cucugliato 9
Workflow
End-point perturbation(Stiffness acquisition)
User FORCE
DISPLACEMENT
Post-processing
SVD
Arm end-pointstiffness estimation Geometrical
representation
10Luisa Cucugliato
Workflow
End-point perturbation(Stiffness acquisition)
User FORCE
DISPLACEMENT
Post-processing
SVD
Arm end-pointstiffness estimation Geometrical
representation
Multiplerobotpositions
RobotController
3
5cm
5cm
Robothandle
Grid ofequidistant points
1 2 4
5 6 7 8
10Luisa Cucugliato
Workflow
End-point perturbation(Stiffness acquisition)
0%
25%
50%
75%
MVC
User FORCE
DISPLACEMENT
Post-processing
SVD
RawEMG
EMGacquisition
Arm end-pointstiffness estimation Geometrical
representation
EMGprocessing Co-contraction indexcomputation
COC
Graphical interface
COCvisual feedback
Multiplerobotpositions
RobotController
3
5cm
5cm
Robothandle
Grid ofequidistant points
1 2 4
5 6 7 8
Luisa Cucugliato 10
Experimental set-up
Robotic arm Forcesensor Inertial-EMGsensors
Collaborativerobot
7-axis(7DoFs)
Low weight (16Kg)
Highperformanceandsafety
KUKALWR4+
MATERIALS
Wearable controldevice
IMUs (9-axis)
8sEMG sensors
M3815C MYOArmband
Load cell
FullWeastonebridgeconfiguration
Luisa Cucugliato 11
Experimental set-up
Robotic arm Forcesensor Inertial-EMGsensors
Collaborativerobot
7-axis(7DoFs)
Low weight (16Kg)
Highperformanceandsafety
KUKALWR4+
Wearable controldevice
IMUs (9-axis)
8sEMG sensors
M3815C MYOArmband
Load cell
FullWeastonebridgeconfiguration
( $
MultipleRobotpositions
MODALITIES
3
5cm
5cm
Robothandle
Grid ofequidistant points
1 2 4
5 6 7 8
0%
25%
50%
75%
MVC
8EEpositionsUser
4COC-Indexlevels
MultipleCo-contracion levels
x
y
Luisa Cucugliato 11
Acquisition protocol
Robotic arm
1
23
4
5
7
86
Visualinterface
Grid ofrobotpositions Rigid Armband
RobotbaseRF
Forcesensor
ShoulderRF
Inertial-EMGsensors
Luisa Cucugliato 12
Implementation: ROSframework
Robotic Arm
Joints torque
Joints angles
250Hz
ForceSensor Inertial-EMGsensor
Kinematiccomputation
50Hz
200Hz
Arm orientation
Muscles EMG
Sub-sampling
2KHz250Hz
250Hz250Hz COC-Index
computation
Forcex3
Torquex3
UserDataset
Luisa Cucugliato 13
COC-Indexcomputation
PROCESS
v Signal normalization
v Acquisition agonist-antagonist EMG
v Pre-processing:
v COC-Indexcomputation
• Offsetremoval• Rectification
• Smoothing (Moving Average)
Luisa Cucugliato 14
COC-Indexcomputation
v Signal normalization
v Acquisition agonist-antagonist EMG
v Pre-processing:
v COC-Indexcomputation
• Offsetremoval• Rectification
• Smoothing (Moving Average)
PROCESS
EMGmax: BicepsEMGmin:Triceps
Luisa Cucugliato 14
COC-Indexcomputation
PROCESS
EMGmax: TricepsEMGmin:Biceps
v Signal normalization
v Acquisition agonist-antagonist EMG
v Pre-processing:
v COC-Indexcomputation
• Offsetremoval• Rectification
• Smoothing (Moving Average)
Luisa Cucugliato 14
FrequencyMethod1/2
Humanhand sphericalpathway
1.Robothandle displacement:stochastic perturbation inspace (20mminx,y,z)
Luisa Cucugliato 15
FrequencyMethod1/2
2.Acquisition oftheforceand displacement at therobotEnd-Effector (250Hz)
ForcesensorM3815C
Inversekinematic(KUKAjoints angles)
1.Robothandle displacement:stochastic perturbation inspace (20mminx,y,z)
LPF:15Hz
LPF:15Hz
Luisa Cucugliato 15
FrequencyMethod1/2
3.F/dMIMOsystem NON-parametric identification frequency response (0-10Hz)
zh(t)
Fx(t)[N]
Fy(t)[N]
Fz(t)[N]
x(t) [m]
y(t) [m]
z(t) [m]
1.Robothandle displacement:stochastic perturbation inspace (20mminx,y,z)
CdF(s)=cross-spectra input-output,Sd(s) =auto-spectra input-inputH(s)=transferfunction output/input
2.Acquisition oftheforceand displacement at therobotEnd-Effector (250Hz)
[3x1] [3x3] [1x3]
i,j=1,2,3
Luisa Cucugliato 15
FrequencyMethod2/2
4.Secondorder fitting ofthenonparametric transferfunction (0-10Hz)
fitting =system zeros
=system pole
[A.Ajoudani andA.Bicchi, “Tele-impedance:Teleoperation withimpedance regulation using abody-machineinterface,” International Journal ofRobotic Research, 2012]
Luisa Cucugliato 16
FrequencyMethod2/2
4.Secondorder fitting ofthenonparametric transferfunction (0-10Hz)
fitting
5.Parametric identification ofthehumanhand dynamic proprieties
Inertia matrix
Damping matrix
Stiffness matrix
Luisa Cucugliato 16
=system zeros
=system pole
Trials
• 2right-handed users
• 4trialsforeachposition(varing theCOC-level)
• 8trialsforeachposition
Stiffness geometricalrepresentation
Results analysis
2x4x8total trials xy
z
End-point stiffness ellipsoid
Luisa Cucugliato 17
Result:Stiffness Ellipses
• Male• 24years old• 185cm
User1: User2: • Female• 23years old• 160cm
User’s shoulderUser’s shoulder
Luisa Cucugliato 18
Result:Stiffness Orientation
𝜙 = angle the major axis formswrt the line connectingshoulder and wrist
x
y𝜙 = 2.8°±1.1° 𝜙 = 2.6°±1.4°
User1 User2
Stiffness ellipses orientation aproximatelly alligned withtheshoulder-wrist axis (directionality)
Shoulder-wrist line Shoulder-wrist line
Luisa Cucugliato 19
Result:Stiffness Orientation
𝜗 =anglethemajoraxisformswrt y-axis onplane
x
y𝜗 = 54.4° − 9.3°
x
y𝜗 = 65.2° − 12.6°
User1 User2
Stiffness ellipses orientation changes according toeachhumanarm position onplane
MajoraxisMajoraxis
Luisa Cucugliato 20
Result:Stiffness Eccentricity
x
y
x
y
User1 User2
Stiffness ellipses eccentricity (Ecc) showsaanisotropic behavior
• Highter eccentricity fordistal positions (Kinematic dependency)• Highter eccentricity forhighco-contraction level (Dynamic depencency)
𝐸𝑐𝑐 = 0.43 ±0.29𝐸𝑐𝑐 = 0.46 ±0.35
Luisa Cucugliato 21
Result:Stiffness Size
25% 50% 75%COC Index:
25% 50% 75%COC Index:
y[m
]
User1
User2
Stiffness ellipses size (ellipse area)is influenced bythemucles co-contraction levels
0.50-0.5-1-1.5
0.50-0.5-1-1.5x [m]
Coc: 25%Coc: 50%Coc: 75%
Coc: 25%Coc: 50%Coc: 75%
1.2
1
0.8
0.6
0.4
0.2
0
-0-2
-0-4
1.2
1
0.8
0.6
0.4
0.2
0
-0-2
-0-4
x [m]
Position4
y[m
]y
[m]
Ellipse size increases withco-contraction index
Luisa Cucugliato 22
Result:Stiffness Size
25% 50% 75%COC Index:
25% 50% 75%COC Index:
y[m
]
User1
User2
Stiffness ellipses size (ellipse area)is influenced bythemucles co-contraction levels
0.50-0.5-1-1.5
0.50-0.5-1-1.5x [m]
Coc: 25%Coc: 50%Coc: 75%
Coc: 25%Coc: 50%Coc: 75%
1.2
1
0.8
0.6
0.4
0.2
0
-0-2
-0-4
1.2
1
0.8
0.6
0.4
0.2
0
-0-2
-0-4
x [m]
Position4
y[m
]y
[m]
Ellipse size increases withco-contraction index
∆𝑠𝑖𝑧𝑒 = 50.61%
Luisa Cucugliato 22
Result:Stiffness Ellipsoidz
[N/m
]
y [N/m] y [N/m]x [N/m]
z[N
/m]
x [N/m]
Position4
Luisa Cucugliato 23
Conclusions andFuturework
Lademomi nonvuolechedicacheilmetodoèoffline
• Onlineestimation ofthehumanarm stiffness bymeans ofthefrequency-basedmethod mplementation
• Evaluationofkinematic anddynamic influences onthestiffness estimated bytheproposed method
Luisa Cucugliato 24
Conclusions andFuturework
Lademomi nonvuolechedicacheilmetodoèoffline
Increasing number oftrials invastigating theinter-subject variability
Implementing the robot controlusing the estimated stiffness ascommand reference
Adopting EMG sensorsmore accurate todistinguish the musclescross-talking
• Onlineestimation ofthehumanarm stiffness bymeans ofthefrequency-basedmethod mplementation
• Evaluationofkinematic anddynamic influences onthestiffness estimated bytheproposed method
Luisa Cucugliato 24
Thursday,December 20,2018
End-point stiffness estimation ofthehumanarm inHuman-RobotInteraction
Supervisor:Prof.ssaElenaDeMomi
LuisaCucugliato872310luisa.cucugliato@mail.polimi.itCo-supervisor:JacopoBuzzi
Thank you!