Contrôle de la locomotion artificielle: Une approche par commande prédictive

1. Contrôle de la locomotion artificielle: Une approche par commande prédictive sans trajectoire de référence Philippe Poignet (LIRMM, Montpellier) Christine Azevedo (INRIA, Grenoble)

2. Context| Human locomotion features | Control approach | Conclusions & perspectives Context 1. Biped robots 2. Locomotion control 3. Guidelines of the research 2

3. Context| Human locomotion features | Control approach | Conclusions & perspectives 1. Biped robots 1. Some realisations Johnnie TUM ASIMO & P3 Honda Motor Co Wabian Waseda University M2 MIT 3

4. Context| Human locomotion features | Control approach | Conclusions & perspectives 1. Biped robots cluttered environments 2. General issues Mobile robots: wheeled, caterpillar, legged Legged robots: n-legs, biped human facilities (stairs, corridors…) Mobility  environment perception & understanding adaptation autonomy Biped  stability skills (contacts, impacts) robustness to disturbances falls paradigms 4

5. Context| Human locomotion features | Control approach | Conclusions & perspectives - trunk + pelvis + 2 legs - 15 active joints: 7 sagittal: ankles, knees, hips, trunk 5 frontal: ankles, hips, trunk 3 horizontal: hips, trunk - 105 kg - 180 cm - human proportions 1. Biped robots 3. BIP: the anthropomorphic robot BIP was designed and built in collaboration between INRIA and LMS Poitiers 5

6. Context| Human locomotion features | Control approach | Conclusions & perspectives Context 1. Biped robots 2. Locomotion control 3. Guidelines of the research 6

7. Context| Human locomotion features | Control approach | Conclusions & perspectives 2. Locomotion control 1. State of the art • Pre-computed reference trajectory tracking • - anthropomorphic joint trajectories [vukobratovic et al 01] • - torque trajectories [goswami et al 96], [pratt & pratt et al 01] • optimal trajectories [chevallereau et al 97], [chessé & bessonnet 01] • Pre-computed movements  non-adaptable to environment and events changes Reference trajectories Control Sensors information Desired behaviour Real behaviour 7

8. Context| Human locomotion features | Control approach | Conclusions & perspectives 2. Locomotion control 1. State of the art (2) • On-line walking adaptation • ZMP compensation[park99] • discrete set of trajectories [denk01] • large set of trajectories needed + switches - continuous set of parameterized trajectories[wieber00][chevallereau02] • defining the set • - learning techniques[kun96] • - neuro-fuzzy [meyret02] • no explicit model 8

9. Context| Human locomotion features | Control approach | Conclusions & perspectives Context 1. Biped robots 2. Locomotion control 3. Guidelines of the research 9

10. Context| Human locomotion features | Control approach | Conclusions & perspectives • 1. no trajectory tracking • 2. high adaptability + no algorithm switches • 3. robustness to disturbances • searching inspiration from human walking without mimicking Reference trajectories Control Sensors information Desired behaviour Real behaviour 11 New approach to biped locomotion control

11. Context | Human locomotion features | Control approach | Conclusions & perspectives Human locomotion features 1. Locomotion structure (biomechanics) 2. Locomotion control (neurosciences) 3. Conclusion: some principles 12

12. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Locomotion structure 1. Walking activity • stationary / transient gait (stop, starting,…) • stationary walk: symmetric + cyclic • phases : support and swing • supports: single support and double support • variable patterns (tiredness, learning…) • objective oriented optimization of displacements (metabolic energy minimization in stationary walk) [vaughan et al 92] 13

13. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Locomotion structure 2. Equilibrium Static equilibrium: CoM projection within support base (posture, difficult situations, working at a work station…) Dynamic equilibrium: normal walking fall forward onto the foot receiving the body‘s weight. Definition remains an open problem for bipedal systems with unilateral constraints. 14

14. Context | Human locomotion features | Control approach | Conclusions & perspectives Human locomotion features 1. Locomotion structure (biomechanics) 2. Locomotion control (neurosciences) 3. Conclusion: some principles 15

15. Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Locomotion control disturbances 1. Control process activation force movement CNS controller muscles actuators skeleton system intention Sensors environment 16

16. disturbances activation force movement CNS controller muscles actuators skeleton system intention Sensors environment 16 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Locomotion control 1. Control process 2. Control properties • No reference trajectory tracking • Anticipation and prediction: CNS internal models  planning • Strategy: library of objective oriented solutions • Learning: taking lessons from past situations

17. Context | Human locomotion features | Control approach | Conclusions & perspectives Human locomotion features 1. Locomotion structure (biomechanics) 2. Locomotion control (neurosciences) 3. Conclusion: some principles 17

18. Context | Human locomotion features | Control approach | Conclusions & perspectives 3. Conclusion: some principles • Unsuccessful approaches in exploiting movements invariants. • 1. Locomotion structure • - Consider both stationary and transient walk • - Optimal gaits / criteria adapted to goal (endurance, speed) • - Consider both static and dynamic equilibrium • 2. Locomotion control • - No reference trajectory tracking • - Perception • - Anticipation and prediction • - Consider internal and external constraints to ensure feasibility and equilibrium. idea: use a model predictive controlapproach 18

19. Context | Human locomotion features | Control approach | Conclusions & perspectives Control approach Use of a model predictive control (MPC) approach: 1. Modelling 2. Model predictive control 3. Application of MPC to locomotion control 4. Simulation results 5. Conclusions 19

20. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling [wieber00] [genot98] [pfeiffer96] 1. Continuous dynamics 1. Lagrange formulation q7 joint positions robot orientation and position in 3D space n dof q8 Depending on the contacts the system can be underactuated 20

21. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (1) 1. Lagrange formulation 2. Ground contact support force n t =(n,t)T 21

22. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (1) 1. Lagrange formulation 2. Ground contact closure constraint: support force n t =(n,t)T 21

23. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (2) n t =(n,t)T 22

24. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (2) n t =(n,t)T unilateral constraint 22

25. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (2) n t =(n,t)T unilateral constraint 22

26. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (2) n t =(n,t)T complementarity condition unilateral constraint 22

27. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 1. Continuous dynamics (2) n t no-slipping assumption (friction cone ) =(n,t)T complementarity condition unilateral constraint 22

28. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics 23

29. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics Impact  velocity jump: =(n,t)T 23

30. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling Impulsive force 2. Impact dynamics t Impact  velocity jump: =(n,t)T n 23

31. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics t Impact  velocity jump: =(n,t)T n 23

32. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics t Impact  velocity jump: =(n,t)T no take-off assumption n 23

33. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics t Impact  velocity jump: =(n,t)T no take-off assumption n no-slipping assumption (friction cone ) 23

34. Context | Human locomotion features | Control approach | Conclusions & perspectives 1. Modelling 2. Impact dynamics t Impact  velocity jump: =(n,t)T no take-off assumption n no-slipping assumption (friction cone ) 23

35. Context | Human locomotion features | Control approach | Conclusions & perspectives Control approach Use of a model predictive control (MPC) approach: 1. Modelling 2. Model predictive control 3. Application of MPC to locomotion control 4. Simulation results 5. Conclusions 24

36. 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon

37. time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon input state k

38. time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon input state Example: elevation of the swing ankle k

39. input state time k k+1 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

40. input state time k k+1 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

41. ? input state time k k+1 k+2 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

42. input state time kk+1 k+2 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

43. ? input state time kk+1 k+2 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

44. input state time k+1k+2 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

45. ? input Obstacle detection state time k+1k+2 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

46. ? input Obstacle state time k+2 k+3 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

47. ? input Obstacle state time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

48. ? input Obstacle state time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

49. ? input Obstacle state time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle

50. ? input No solution !!! Obstacle state time 25 Context | Human locomotion features | Control approach | Conclusions & perspectives 2. Model predictive control 1. Control without predictive horizon Example: elevation of the swing ankle