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DHM 2011 presentation

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With this slideshow I presented the work "A hybrid dynamic motion prediction method with collision detection" at the DHM 2011 conference.

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DHM 2011 presentation

  1. 1. A hybrid dynamic motion prediction method with collision detection Ilaria Pasciuto Alexander Valero Sergio Ausejo Juan Celigüeta14-16/06/2011 DHM 2011, Lyon 1
  2. 2. Introduction • Integration of DHMs in product design www.dhergo.org • Human motion prediction: – To simulate the interaction of different user populations with a variety of environments – Aims: realism and representativeness14-16/06/2011 DHM 2011, Lyon 2
  3. 3. Motion prediction methods • Data-based – Resemble reference motion min f(q)! • Knowledge-based – Follow motion control law14-16/06/2011 DHM 2011, Lyon 3
  4. 4. Data-based methods • Currently only kinematic • Reference motion from database • Modification to meet new goals • Advantages: – Intrinsic realism of reference motion – Suitable for complex task-oriented motions • Drawbacks: – Only predict tasks in database14-16/06/2011 DHM 2011, Lyon 4
  5. 5. Knowledge-based methods • Currently also dynamic • No reference min f(q)! • Realism through motion control law • Advantages: – Theoretically applicable to any task • Drawbacks: – Appropriate motion control law is difficult to identify14-16/06/2011 DHM 2011, Lyon 5
  6. 6. Novel prediction method • Hybrid dynamic motion prediction method: data-based & knowledge-based min f(q)! Data-based Dynamics Knowledge-based Applied to the prediction of a clutch-pedal depression14-16/06/2011 DHM 2011, Lyon 6
  7. 7. Methodology Data-based prediction Experimental data Motion prediction Motion Database reconstruction generation Reference motion selection End-effector trajectory modification Human model Optimization definition14-16/06/2011 DHM 2011, Lyon 7
  8. 8. Methodology Data-based prediction Motion Experimental Knowledge control law data Motion prediction Motion Database reconstruction generation Reference motion selection End-effector trajectory modification Human model Optimization with Dynamics definition14-16/06/2011 DHM 2011, Lyon 8
  9. 9. Methodology Data-based prediction Motion Experimental Knowledge control law data Motion prediction Motion Database reconstruction generation Reference motion selection End-effector trajectory modification Human model Optimization with Dynamics definition14-16/06/2011 DHM 2011, Lyon 9
  10. 10. Human model definition • Multi-body model • Left leg: – 4 segments • Pelvis • Thigh • Shank • Foot – 13 DoFs • 10 rotations • 3 translations • RAMSIS specifications • Described with relative coordinates14-16/06/2011 DHM 2011, Lyon 10
  11. 11. Reference motion selection • Resemblance in scenariosPred. Scenario Database Ref. Scenario Pred. Subject Subject 1 Subject 1 Subject N Subject J Pred. Environment Environment … Environment Environment Environment 1 2 M K • Subject data • Environment data • Gender • Age • Stature • Weight14-16/06/2011 DHM 2011, Lyon 11
  12. 12. End-effector trajectory modification • Reference and prediction scenarios are different • Modification of reference trajectory to meet new goals – 2 methods (Zhang 2002): Velocity proportional Acceleration preserving  ˆ x  c1 x ˆ x c1 x c2  xˆ  x ˆ x x c1 t c214-16/06/2011 DHM 2011, Lyon 12
  13. 13. Dynamic prediction • Predicted motion must: – Fulfill new goals – Resemble reference motion – Follow dynamic motion control law – Ensure dynamic equilibrium of DHM14-16/06/2011 DHM 2011, Lyon 13
  14. 14. Dynamic prediction • Predicted motion must: – Fulfill new goals – Resemble reference motion – Follow dynamic motion control law – Ensure dynamic equilibrium of DHM Relates frames one another14-16/06/2011 DHM 2011, Lyon 14
  15. 15. Dynamic prediction • Predicted motion must: – Fulfill new goals – Resemble reference motion – Follow dynamic motion control law – Ensure dynamic equilibrium of DHM Relates frames one another • B-spline representation of DoF profiles – Motion considered as a whole (not per-frame)14-16/06/2011 DHM 2011, Lyon 15
  16. 16. Optimization • Design variables: B-spline control points • Constrained optimization – Objective function – Constraints14-16/06/2011 DHM 2011, Lyon 16
  17. 17. Optimization • Design variables: B-spline control points • Constrained optimization – Objective function • Resemble reference DoF profiles • Resemble modified end-effector trajectory • Follow motion control law: resemble reference joint power profiles (Pasciuto et al., 2010) – Constraints14-16/06/2011 DHM 2011, Lyon 17
  18. 18. Optimization • Design variables: B-spline control points • Constrained optimization – Objective function • Resemble reference DoF profiles • Resemble modified end-effector trajectory • Follow motion control law: resemble reference joint power profiles (Pasciuto et al., 2010) – Constraints • Fulfill new goals • Ensure dynamic equilibrium of DHM14-16/06/2011 DHM 2011, Lyon 18
  19. 19. Collision detection • To evaluate external contact forces • Environment reacts with a force according to the position and velocity of the DHM14-16/06/2011 DHM 2011, Lyon 19
  20. 20. Collision detection • To evaluate external contact forces • Environment reacts with a force according to the position and velocity of the DHM Depends on pedal position, Depend on due to foot pelvis position position respect to the seat14-16/06/2011 DHM 2011, Lyon 20
  21. 21. Results (I) • End-effector trajectory -0.7 End Effector Trajectory X Axis [m] Reference Modified Predicted -0.75 Depress -0.8 -0.85 Reach -0.9 0 20 40 60 80 100 Time [%]14-16/06/2011 DHM 2011, Lyon 21
  22. 22. Results (I) • End-effector trajectory -0.7 End Effector Trajectory X Axis [m] Reference Predicted Modified Validation1 Predicted Validation2 -0.75 Validation3 Depress -0.8 -0.85 Reach -0.9 0 20 40 60 80 100 Time [%]14-16/06/2011 DHM 2011, Lyon 22
  23. 23. Results (II) • Knee flexion-extension joint angle profile 90 Reference Predicted GKNL Flex-Ext Angle [deg] 80 Validation1 Validation2 Validation3 70 Reach 60 50 Depress 40 0 20 40 60 80 100 Time [%]14-16/06/2011 DHM 2011, Lyon 23
  24. 24. Results (III) • Hip flexion-extension joint torque profile 150 Depress Reference GHUL Flex-Ext Torque [Nm] Predicted Reach Validation1 100 Validation2 Validation3 50 0 -50 0 20 40 60 80 100 Time [%]14-16/06/2011 DHM 2011, Lyon 24
  25. 25. Results (IV) • Forces acting on the pelvis 150 Error Child Depress Force in Pelvis X Axis [N] 100 Inertia External 50 0 -50 Reach -100 -150 0 20 40 60 80 100 Time [%]14-16/06/2011 DHM 2011, Lyon 25
  26. 26. Conclusions • Hybrid method for dynamic motion prediction – Combines data-based and knowledge-based methods – Dynamics: ensures equilibrium • Collisions – Detects and models collisions between DHM and environment • Validation – Compared against 3 repetitions of the motion carried out in the prediction scenario14-16/06/2011 DHM 2011, Lyon 26
  27. 27. Acknowledgements • European Project FP7 “DHErgo”: Digital Humans for the Ergonomic design of products • Partners: BMW, RENAULT, PCA, CEIT, IFFSTAR, ULB, TUM, HS, ESI, ERT14-16/06/2011 DHM 2011, Lyon 27
  28. 28. A hybrid dynamic motion prediction method with collision detection Thank you for your attention! Ilaria Pasciuto ipasciuto@ceit.es Alexander Valero Sergio Ausejo Juan Celigüeta14-16/06/2011 DHM 2011, Lyon 28

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