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Tendon topology inference : Update meeting

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.

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Tendon topology inference : Update meeting

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  1. Tendon topology inference : Update meeting Manish Kurse March 29, 2011

  2. 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

  3. Following up on a point raised in my qualifying exam : • Why do the complex inference? How bad are existing models? An et al. 1983 UI UB ES TE EDC RB LU and RI TE=RB+UB RB=0.133 RI+0.167 EDC+0.667 LU UB=0.313 UI+0.167 EDC ES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU Valero Cuevas et al. 1998 Chao et al. 1978,79

  4. Evaluation of existing models of the extensor mechanism (Submitted to ASB 2011) P1 P2 P3

  5. These models don’t match, but does that justify inference of topology and parameters? • Optimization of normative model UI UB ES TE EDC RB LU and RI TE=RB+UB RB=0.133 RI+0.167 EDC+0.667 LU UB=0.313 UI+0.167 EDC ES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU Chao et al. 1978,1979

  6. Hill climber optimization : 8 parameters UI UB ES TE EDC RB LU and RI TE=RB+UB RB=0.133 RI+0.167 EDC+0.667 LU UB=0.313 UI+0.167 EDC ES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU Chao et al. 1978,1979

  7. P3 P2 P1

  8. 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

  9. Tendon network inference • To begin optimization : FEM solver should be fast and robust. FEM solver : several updates.

  10. Optimization of network on solid • Simple problem. • Topology fixed, parameter optimization. • Start with very simple problem

  11. Cross On Hemisphere α

  12. Hill climber optimization Cost fun : Normalized error in Reaction forces at the fixed nodes α Converged to optimum solution : alpha = 0.5

  13. Multi – parameter optimization A3 5 Parameters : alpha (fraction defining location of node) AND 4 cross sectional areas of the 4 elements : A1, A2, A3, A4 A2 A1 A4 α

  14. Hill climber optimization Cost fun : Normalized error in Reaction forces at the fixed nodes A3 A2 A1 Non uniqueness and problem of observability : Soln : More data points? A4 α

  15. 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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