Embed Size (px)
Citation preview
Georgia Institute of Technology | Marquette University | Milwaukee School of Engineering | North Carolina A&T State University | Purdue University | University of California, Merced | University of Illinois, Urbana-Champaign | University of Minnesota | Vanderbilt University
Generic Presentation Template for CCEFPUpdate this text in the Slide Master
Coordinated Rate Control User Interface and Task Identification of an Excavator
BeauDomingue,GRAGeorgiaIns3tuteofTechnology
Prof.WayneBook
FPIRC2016
Research Goal Makefluidpower:• Moreefficient• Safer• EasiertoUse
ConsolidateresultsonmulDdegreeoffreedominterfacesovertherangeofspeeds,dimensions,numbersofinterfaces,extentofautomaDonandinterfacemodaliDesfoundwithhydraulicactuaDon.
FPIRC2016
GT Excavator Simulator
FPIRC2016
GT Excavator Simulator
FPIRC2016
Implement On Live Hardware • ExcavatorSimulatorisagreattool,butnotrealthing
• Verifyresultsonlivemachine• Doneinpast,wouldliketodoagaininthefuture
FPIRC2016
3D – Stereo on GT Simulator
FPIRC2016
3D – Stereo on GT Simulator
FPIRC2016
Excavator User Interfaces" • Thecurrentstateoftheart• TheevoluDonofthetechnologydevelopedforthisproject.
FPIRC2016
Conventional Excavator UI • JointflowcontrolwithjoysDcks
• NotintuiDve• Slowlearningcurve• RequiresextensivetrainingImprovedUIWould:• ReducecogniDveload• Reduceoperatorerrors• ReducetaskDme&fuelconsumed
FPIRC2016
State of Art Excavator UI
FPIRC2016
Jacobian Breakdown of Control Vectors
FPIRC2016
Jacobian Breakdown of Control Vectors
FPIRC2016
Coordinated Control • MapsoutputofmulDdegreeoffreedomsystemtocoordinatesystemfamiliartooperator
• ChangeinputsofexcavatortocontrolendeffectorposiDondirectly,notindirectlythroughjointangles.
• ControllersolvinginversekinemaDcs,notuser
FPIRC2016
Coordinated Control • PosiDonorRateControl?
FPIRC2016
Coordinated Rate Control with Phantom
FPIRC2016
Coordinated Position Control with Phantom
FPIRC2016
Position vs. Rate Control on Excavator • Excavatorsareslower,andfavor
ratecontrol• ReducedeffecDvenessof
posiDonalcontrolduetofactthattheoperatordoesn’tknowwhatcommandisbeinggiven,leadstoresponseovershootorundershoot
• EltonconfirmedthisbytesDngposiDoncontrolwithghost,whichperformedsignificantlybeXer
• ApplicableonremoteoperaDon
FPIRC2016
Conventional Control vs. Coordinated Rate Control with Phantom
FPIRC2016
Phantom Roadblocks
Fragile ExpensiveNotErgonomic(fa3gueissues)
BiodynamicFeedthrough
FPIRC2016
Coordinated Position Control - Kinematically Similar Arm
Fa3gue+biodynamicfeedthroughissues!
FPIRC2016
Coordinated Position - Control Kinematically Similar Arm
RotatecontrolplanesidewaystoaddressfaDgueissues
FPIRC2016
Coordinated Position Control Kinematically Similar Arm
RotatedcontrolplaneinconsequenDaltoperformancemetrics
FPIRC2016
User Interface Adoption Players • Manufacturers
– Price– Complexity– Durability
• Contractors/Owners– Liability– FuelEfficiency
• Operators– Ergonomics– TimeEfficiency– LearningCurve
Goal:Implementnewcontroller,consideringallconcerns,usingwhatwe’velearnedfrompreviouswork
FPIRC2016
Proposed Configuration • NewUIwouldnotrequirenewhandcontrollers• NewUIshouldreapbenefitsassociatedwiththechangefromjointcontroltocoordinatedcontrolandoffersignificantperformanceimprovements
• ExcavatorwouldbecapableoftogglingbetweentradiDonalcontrolstyleandnewstyleviaswitch
• Requirements:excavatormustbeawareofitsjointangles,andhavecomputerordedicatedchipperformthemathemaDcsassociatedwithcontroller
JoysDck HydraulicsFlowCommands JoysDck
Hydraulics
CoordinatedCommands
ExcavatorJointAngles
Controller
FlowCommands
TradiDonalorCoordinatedSwitch
FPIRC2016
New Coordinated Rate Control Mapping?
FPIRC2016
New Coordinated Rate Control Mapping
FPIRC2016
Mapping – Left Joystick
FPIRC2016
Mapping - Right Joystick
FPIRC2016
Conventional Vs. CRC
TradiDonalControl CoordinatedRateControl(CRC)
FPIRC2016
Human Subject Experiment
Session 1 Session 2 Session 3
warm-up | trial1 | trial2 | trial3 warm-up | trial1 | trial2 | trial3 warm-up | trial1 | trial2 | trial3
Group A Conventional Conventional CRC
Group B CRC CRC CRC
FPIRC2016
Time Efficiency
Trialmean 1 2 3 4 5 6
Soil
Rem
oved
(kg)
/ Ti
me
(min
)
0
200
400
600
800
1000
Conv. UICRC UI
FPIRC2016
Group Time Efficiency
Trialmean 1 2 3 4 5 6 7 8 9
Soil
Rem
oved
(kg)
/ Ti
me
(min
)
0
200
400
600
800
1000
Group AGroup B
FPIRC2016
Fuel Efficiency
Trialmean 1 2 3 4 5 6
Soil
Rem
oved
(kg)
/ Fu
el C
onsu
med
(kg) #104
0
0.5
1
1.5
2
2.5
3
Conv. UICRC UI
FPIRC2016
Idle Time
Trialmean 1 2 3 4 5 6
Perc
enta
ge o
f Tim
e Sp
ent I
dle
0
10
20
30
40
50Conv. UICRC UI
FPIRC2016
Survey Results
Conventional CRC No Preference0
2
4
6
8
Num
ber o
f Sub
ject
s
Group A subjects Learning Preference
Expert
Conventional CRC No Preference0
1
2
3
4
5
6
7
Num
ber o
f Sub
ject
s
Group A Subjects Bucket Control Preference
Expert
Conventional CRC No Preference0
1
2
3
4
5
Num
ber o
f Sub
ject
s
Group A Subjects Controller Confidence
Expert
FPIRC2016
Task Identification
FPIRC2016
Standard Trenching Cycle
ReturnDig Unload
FPIRC2016
Task Identification • Trenchingprocesscanbedelineatedintothreesubtasksorphases:– Digphase– Unloadphase– Returnphase
• Duetothewelldefinedcyclenatureoftheprocess,asupervisedclassificaDonapproachcanbeused.
FPIRC2016
Artificial Neural Network (2ANN)
Input Layer (2)
Hidden Layer (50)
Output Layer (3)
2x50 Weights
50x3 Weights
Dig
Unload
Return
FPIRC2016
2ANN Classification of seen Training Data
1 2 3 4 5−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
SwingStickDigUnloadReturn
ManualClassification
ANNClassification
FPIRC2016
2ANN Classification of seen Training Data
2−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
SwingStickDigUnloadReturn
ManualClassification
ANNClassification
FPIRC2016
2ANN performance • TheresulDngclassificaDonwas~96%accurateforthetrainingdatawithclassificaDoncorrecDons.
• However,theANNwasnotrobusttooperatorstyleduetoitsextremelysimplestructureandresulDngdecisionboundary.
FPIRC2016
2ANN Decision Boundary
−1 −0.5 0 0.5 1−1
−0.5
0
0.5
1
Swing
Stic
k
DigUnloadReturn
FPIRC2016
2ANN Unseen Data Classification
2 3−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
SwingStickDigUnloadReturn
{ 2 1 3 1 3 1 }
FPIRC2016
2ANN Unseen Data Classification
2 3−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
SwingStickDigUnloadReturn
Premature Transition
{ 2 3 1 }
FPIRC2016
Training Data Generation • The2ANNgenerallyyieldedpoorclassificaDonsforunseendata.
• However,withsomepreandpostprocessingofclassificaDons(*thatcouldnotbedoneinrealDme)the2ANNyieldedacceptableresultsforsomedatasets.
• Thus,the2ANNwasusedtogeneratetrainingdatatotrainanotherANN.
FPIRC2016
Training Data Generation • 5datasets,correspondingto5trialsfromdifferentsubjectsforeachgroup,wereselected.
• Datawassampledat100Hzeach5minutetrial,correspondingto30,000datapoints,or150,000datapointstotal.
FPIRC2016
Artificial Neural Network (8ANN)
Input Layer (8)
Hidden Layer (50)
Output Layer (3)
8x50 Weights
50x3 Weights
VelocityCommands
JointPositions
Dig
Unload
Return
FPIRC2016
8ANN Classification of seen Training Data
1 2 3 4 5−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
Swing Vel cmdBoom Vel cmdStick Vel cmdBucket Vel cmdSwing PosDigUnloadReturn
ManualClassification
ANNClassification
FPIRC2016
8ANN Classification of seen Training Data
2−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
Swing Vel cmdBoom Vel cmdStick Vel cmdBucket Vel cmdSwing PosDigUnloadReturn
ManualClassification
ANNClassification
FPIRC2016
8ANN Classification of unseen Data
2 3−1
−0.5
0
0.5
1
Time (min)
Nor
mal
ized
Inpu
t
Swing Vel cmdBoom Vel cmdStick Vel cmdBucket Vel cmdSwing PosDigUnloadReturn
Swing Right Swing LeftRobust to Oscillation/Glitches
FPIRC2016
Visualization of subspace of 8 Dimensional Decision Boundary
1-1-1
Boom
0
0
Stic
k
Swing
0
1
-11
DigUnloadReturnBucket ClosingBucket nullBucket Opening
FPIRC2016
Visualization of subspace of 8 Dimensional Decision Boundary
FPIRC2016
Thanks to Contributors and Partners
