tendon topology inference : update meeting
DESCRIPTION
Tendon topology inference : Update meeting. Manish Kurse March 29, 2011. Overview. Use of Eureqa in inference of analytical expressions for tendon movements of a robotic finger: First draft ready : Fco currently reviewing. Validation of existing models of the finger’s tendon networks. - PowerPoint PPT PresentationTRANSCRIPT
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Tendon topology inference : Update meeting
Manish KurseMarch 29, 2011
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Overview
• Use of Eureqa in inference of analytical expressions for tendon movements of a robotic finger: First draft ready : Fco currently reviewing.
• Validation of existing models of the finger’s tendon networks.
• Inference of tendon network topology and parameters - updates
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• Following up on a point raised in my qualifying exam : – Why do the complex inference? How bad are existing
models?
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TE=RB+UBRB=0.133 RI+0.167 EDC+0.667 LUUB=0.313 UI+0.167 EDCES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU
TE
LU and RI
EDC
UI
RB
UB E
S
Chao et al.
1978,79Valero Cuevas et al. 1998
An et al. 1983
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Evaluation of existing models of
the extensor mechanism
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(Submitted to ASB 2011)
P1 P2
P3
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These models don’t match, but does that justify inference of topology and parameters?
• Optimization of normative model
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TE=RB+UBRB=0.133 RI+0.167 EDC+0.667 LUUB=0.313 UI+0.167 EDCES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU
TE
LU and RI
EDC
UI
RB
UB ES
Chao et al. 1978,1979
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Hill climber optimization : 8 parameters
TE=RB+UBRB=0.133 RI+0.167 EDC+0.667 LUUB=0.313 UI+0.167 EDCES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU
TE
LU and RI
EDC
UI
RB
UB
ES
Chao et al. 1978,1979
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7P1
P2
P3
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• Qualms :– How good is good enough?– Is this convincing enough to the committee that we need
simultaneous inference of topology and parameters?– I could go on with this optimization of biomechanical
models.
• But the goal of my PhD is not just to model the finger tendon networks, instead– Introduction of the concept of automatic inference of
complex biomechanical models from sparse data. – The fingers’ tendon networks happens to be an example
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Tendon network inference• To begin optimization : FEM solver should be fast and robust.
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FEM solver : several updates.
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Optimization of network on solid
• Simple problem.• Topology fixed, parameter
optimization.• Start with very simple problem
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Cross On Hemisphere
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α
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12Converged to optimum solution : alpha = 0.5
α
Hill climber optimizationCost fun : Normalized error in Reaction forces at the fixed nodes
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Multi – parameter optimization
5 Parameters : alpha (fraction defining location of node)
AND4 cross sectional areas of the 4 elements : A1, A2, A3, A4
α A1
A2
A3
A4
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α A1
A2
A3
A4
Non uniqueness and problem of observability :Soln : More data points?
Hill climber optimizationCost fun : Normalized error in Reaction forces at the fixed nodes
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• Conclusions:– For the first time optimization of an elastic tendon
network on an arbitrary solid. – Simple network : Parameter optimization successful.
• Next steps :– Fixed topology parameter estimation.
• Extensor mechanism on hemisphere , finger
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