David Galdeano. LIRMM-UM2, Montpellier, France. Members of CST: Philippe Fraisse, Ahmed Chemori, Sébatien Krut and André Crosnier

Transcription

1 David Galdeano LIRMM-UM2, Montpellier, France Members of CST: Philippe Fraisse, Ahmed Chemori, Sébatien Krut and André Crosnier Montpellier, Thursday September 27, 2012

2 Outline of the presentation Context and motivations Used tools Motion capture system (VICON) Developed simulator Proposed control scheme Simulation results Conclusion Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO

3 Context and motivations Humanoid walking [Kajita, IROS 2010] Dynamically stable Constructed using various phases Discontinuous How to produce natural motion for humanoid robot? Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO

4 Context and motivations Human walking Dynamically stable Energetically efficient No phases Smooth Continuous Human-like walking is an obvious goal for humanoid robotics Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO

5 A typical control architecture in humanoid robotics Pattern generator Walking Parameters Pattern generator feet/zmp positions Robot Model SHERPA robot q d Controller Articulations control U Robot SHERPA robot Contact forces control q, qp Feet position modification Contact forces measurement Posture Foot landing control Position error of the body Balance control Posture control Stabilizer 05/07/2012 D. Galdeano IFAC SYROCO

6 Context and motivations Human motions Simulated model or robot Control scheme Objective : Using of human motion data to improve the humanoid dynamic stability and energy consumption during walking Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO

7 State of art Whole body motion control Task-based schemes Human-Data based schemes Class 1: Online computation Class 2: Offline computation Objective function [Liegeois, 1977] Task priority based redundancy control [Siciliano et al.,1991, Nakamura et al., 1987] Stack of task [Mansard., 2007] Balance/Tracking controller [Yamane et al., ] Imitation [Shaal et al., , Calderon & Hu 2005] Motion Primitives [Nakaoka et al., ] Gait parameter extraction [Harada et al.,2009] Scale and optimization [Suleiman et al., 2008] Human Normalized model [Montecillo et al.,2010] 7

8 Human data based whole body motion control using offline calculation [Nakaoka et al., 2003, 2005] Data from human motion capture are used as motion primitives to produce postural imitation (only postural motions, no walking). [Harada et al., 2009] Data from human motion capture are used to find gait s parameters. [Suleiman et al., 2008] Data from human motion capture are first scaled to humanoid joint position, then an optimization with constraint is used. Pros: Offline computations allows optimized motions Cons: Offline computation do not allow reactive motions 8

9 Human data based whole body motion control using online calculation [Schaal, 1999 ; Schaal et al., 2003 ; Calderon & Hu, 2005] Data from human motion capture are used to feed a learning system to produce accurate movement primitives. [Yamane & Hodgins, 2009 ; Yamane et al., 2010] Two controllers are used in this application. First controller : a balance controller. Second controller : joint space trajectory tracking Pros: Reactive motions using feedback from sensors Cons: No walking motions are reproduced 9

10 Tasks based whole body motion control [Liegeois, 1977, Siciliano et al.,1991, Nakamura et al., 1987, Mansard., 2007] Task formalism is used to distribute the motion on every degrees of freedom. Pros: Efficient to achieve several objectives Cons: Not based on human motion [Montecillo-Puente et al., 2010] Data from human motion capture are performed in real time to produce postural imitation (postural motion, no walking). 10

11 Motivations Reference motion : from human motion capture Differences: Flexible/Rigid Different DoF Different Power Contacts Similarities: CoM Feet cycle Reduced set of human data: Relative feet position (3) + CoM (3) Articular trajectories (22) 11

12 Motivations Basic idea: Task-priority formalism [Nakamura, 1987] How : Two tasks - Relative feet position tracking. - CoM trajectory tracking. Advantages: Continuous control framework: No decomposition into distinct phases, one control law. 12

13 Motion Capture system Context: Walking motion analysis project LABLAB, University of Rome Foro Italico, Pr. Capozzo, Department of Human Movement and Sports Sciences. Equipment : 1 host PC 10 Vicon cameras 3 Forces plates 13

14 Motion Capture system Study: 15 Subjects Different walking speed 35 markers using Plug-in Gait template Reconstruction of movement using Vicon Nexus Estimation of CoM using Lifemod 14

15 Developed simulator ZMP articulations Contact forces with ground Includes whole-body models of different robots. Kinematic and Dynamic model. Contact model (Multi-contacts). Link in tree representation allows easy modification. Modular approach allows to switch between different models. Written in C. OpenGL for graphics. GSL for matrix calculations. 15

16 Developed simulator Newton-Euler dynamic formulation : Avec: allow an recursive computation of rigid multi body system dynamic. Then, dynamic is expressed on canonical form: With: : Mass matrix. : Applied torques. : Coriolis centrifugal matrix. : Contact forces. : Gravity terms. 16

17 Developed simulator Contact model: Contact are calculated as spring mass damper system as describe in [Rodas, 2010] with 4 or more contacts point by foot: A saturation function a prevents a negative contact force ( lift off. ) on Foot Ground 17

18 Prioritized tasks What is a task? Jacobian of relative feet position tracking task is defined as: Articular position error: With null-space projection: 18

19 Basic idea of the proposed control scheme Motion Capture Simulated model Control scheme 19

20 First task Block diagram of the proposed control scheme Second task Null space projection 20

21 Mathematical formulation of the proposed control scheme Jacobian of relative feet position tracking task is defined as: Articular position error: Jacobian of CoM tracking task defined as: Final formulation: With: 21

22 Simulation results Simulation of the proposed control scheme: Data form human motion capture without adaptation 22

23 Simulation results Simulation of the proposed control scheme: Data form human motion capture with scaling of the CoM trajectories 23

24 Simulation results Simulation of the proposed control scheme: Hip trajectories 24

25 Simulation results Simulation of the proposed control scheme: Left leg articular trajectories in the sagittal plane 25

26 Simulation results Simulation with Dynamic: No feet movement CoM moves up and down 26

27 Experimentation results First experimental results using HOAP3 robot: No feet movement CoM moves up and down Need more verifications before experiments on the ground Scaling issues with walking motions 27

28 Conclusion In cinematic simulation: Whole body motion based on a reduced set of human data No distinct phases decomposition Continuous control framework Human-like walking without imitation In Dynamic simulation: Whole body motion based on sinusoid functions Use of tasks framework Issues with walking motions (stability) In Experimentation: Whole body motion based on sinusoid functions Use of tasks framework Issues with walking motions (Scaling) 28

29 Short term perspectives Design a general scaling procedure. Design of a controller for dynamic simulations. ZMP in the control scheme. Real-time implementation of walking motion on a humanoid robot : HOAP3. Long term perspectives Stability and robustness analysis. Energetic consumption analysis. Propose some complementary tasks to improve the autonomy. Study of arms motions effects on energy consumption. Implementation in other robot architecture: SHERPA, HRP4. 29

30 Publications Accepted paper: David Galdeano, Vincent Bonnet, Moussâb Bennehar, Philippe Fraisse and Ahmed Chemori, Partial Human Data in Design of Human-Like Walking Control in Humanoid Robotics. 10th international IFAC symposium on robot Control (SYROCO2012), Dubrovnik, Croatia, September 05-07, Presentations: D. Galdeano, A. Chemori, S. Krut, Optimal Pattern Generator Based on a Three-Mass Linear Inverted Pendulum Model for Dynamic Walking, Workshop on Humanoid and Legged Robots, HLR 2011, Paris, France, February 14-15,

31 RÉFÉRENCES [Calderon & Hu 2005] Calderon, C., & Hu, H. (2005). Robot imitation from human body movements. In In proceeding third international symposium on imitation in animals and artifacts (aisb 05). [Galdeano et al.,2012] Galdeano, D., Bonnet, V., Bennehar, M., Fraisse, P., & Chemori, A., (2012). Partial Human Data in Design of Human-Like Walking Control in Humanoid Robotics. 10th international IFAC symposium on robot Control (SYROCO2012), September 05-07, [Harada et al.,2009] Harada, K., Miura, K., Morisawa, M., Kaneko, K., Nakaoka, S., Kanehiro, F., et al. (2009). Toward human-like walking pattern generator. In Proceedings of the 2009 ieee/rsj international conference on intelligent robots and systems (iros 09) (pp ). Albuquerque, NM, USA. [Montecillo et al.,2010] Montecillo-Puente, F., Sreenivasa, M., & Laumond, J. (2010). On realtime whole-body human to humanoid motion transfer. In International conference on informatics in control, automation and robotics (icinco

32 RÉFÉRENCES [Nakaoka et al., 2005] Nakaoka, S., Nakazawa, A., Kanehiro, F., Kaneko, K., Morisawa, M., & Ikeuchi, K. (2005). Task model of lower body motion for a biped humanoid robot to imitate human dances. In Proceedings ofinternational conference on intelligent robots and systems (iros 05) (pp ). [Nakaoka et al., 2003] Nakaoka, S., Nakazawa, A., Yokoi, K., Hirukawa, H., & Ikeuchi, K. (2003). Generating whole body motions for a biped humanoid robot from captured human dances. In Proceedings of international conference on robotics and automation (icra 03) (Vol. 3, pp ). [Rodas, 2010] RODAS, C. R. (2010). Contributions à la commande d un robot bipède 3d. IRCCYN, Université de Nantes : PhD thesis. [Schaal, 1999] Schaal, S. (1999). Is imitation learning the route to humanoid robots. Trends in cognitive sciences, 3(6), [Schaal et al., 2003] Schaal, S., Ijspeert, A., & Billard, A. (2003). Computational approaches to motor learning by imitation. Philosophical Transactions of the Royal Society of London. Series B : Biological Sciences, 358(1431),

33 RÉFÉRENCES [Suleiman et al., 2008] Suleiman, W., Yoshida, E., Kanehiro, F., Laumond, J., & Monin, A. (2008). On human motion imitation by humanoid robot. In Proceedings of international conference on robotics and automation (icra 08) (pp ). [Takanishiet al., 1989] Takanishi, A., Tochizawa, M., Karaki, H., & Kato, I. (1989). Dynamic biped walking stabilized with optimal trunk and waist motion. In Proceedings of international workshop on intelligent robots and systems : The autonomous mobile robots and its applications (iros 89) (pp ). [Yamaguchi et al.,1993] Yamaguchi, J., Takanishi, A., & Kato, I. (1993). Development of a biped walking robot compensating for three-axis moment by trunk motion. In Proceedings of international conference on intelligent robots and systems (iros 93) (Vol. 1, pp ). [Yamane et al.,2010] Yamane, K., Anderson, S., & Hodgins, J. (2010). Controlling humanoid robots with human motion data : Experimental validation. In Proceedings of international conference on humanoid robots (humanoids 10) (p ). 33

34 [Yamane et al.,2009] Yamane, K., & Hodgins, J. (2009). Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. In Proceedings of international conference on intelligent robots and systems (iros 09) (pp ). [Mansard et al.,2007] Mansard, N., & Chaumette, F. (2007) Task Sequencing for High Level Sensor-Based Control. IEEE Transactions on Robotics. [De Lasa et al.,2010] Mordatch, I., De Lasa, M., & Hertzmann, A. (2010) Robust Physics-Based Locomotion Using Low-Dimensional Planning. ACM Transactions on Graphics, (Proc. SIGGRAPH). 34