Trajectory Generation from Motion Capture for a Planar Biped Robot in Swing Phase
Main Article Content
Keywords
biped robot, motion capture, trajectory generation, dynamic model
Abstract
This paper proposes human motion capture to generate movements for the right leg in swing phase of a biped robot restricted to the sagittal plane. Such movements are defined by time functions representing the desired angular positions for the joints involved. Motion capture performed with a Microsoft Kinect TM camera and from the data obtained joint trajectories were generated to control the robot’s right leg in swing phase. The proposed control law is a hybrid strategy; the first strategy is based on a computed torque control to track reference trajectories, and the second strategy is based on time scaling control ensuring the robot’s balance. This work is a preliminary study to generate humanoid robot trajectories from motion capture.
PACS: 87.85.St
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References
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[2] B. Rosenhahn, R. Klette, and D. Metaxas, Human Motion: Understanding, Modelling, Capture, and Animation. Springer, 2008.
[3] K. Abdel-Malek and J. Arora, Human Motion Simulation: Predictive Dynamics. Elsevier Science, 2013.
[4] A. Menache, Understanding motion capture for computer animation, 2nd ed. Elsevier, 2011.
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[6] J. P. Holden, J. A. Orsini, K. L. Siegel, T. M. Kepple, L. H. Gerber, and S. J. Stanhope, “Surface movement errors in shank kinematics and knee kinetics during gait,” Gait & Posture, vol. 5, no. 3, pp. 217 – 227, 1997.
[7] C. Reinschmidt, A. van den Bogert, B. Nigg, A. Lundberg, and N. Murphy, “Effect of skin movement on the analysis of skeletal knee joint motion during running,” Journal of Biomechanics, vol. 30, pp. 729–732, 1997.
[8] C. D. Mutto, P. Zanuttigh, and G. M. Cortelazzo, Time-of-Flight Cameras and Microsoft Kinect(TM). Springer Publishing Company, Incorporated, 2012.
[9] G. Du, P. Zhang, J. Mai, and Z. Li, “Markerless kinect-based hand tracking for robot teleoperation,” International Journal of Advanced Robotic Systems, vol. 9, no. 10, 2012.
[10] L. A. Schwarz, A. Mkhitaryan, D. Mateus, and N. Navab, “Human skeleton tracking from depth data using geodesic distances and optical flow,” Image and Vision Computing, vol. 30, no. 3, pp. 217 – 226, 2012.
[11] S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, and A. Fitzgibbon, “Kinect-fusion: Real-time 3d reconstruction and interaction using a moving depth camera,” in Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, ser. UIST ’11. New York, NY, USA: ACM, 2011, pp. 559–568.
[12] K. Munirathinam, S. Sakkay, and C. Chevallereau, “Dynamic motion imitation of two articulated systems using nonlinear time scaling of joint trajectories,” in International Conference on Intelligent Robots and Systems (IROS), Algarve, Portugal, May-June 2012.
[13] M. Vukobratović, “Zero-moment point. thirty five years of its life,” International
Journal of Humanoid Robotics, vol. 01, no. 01, pp. 157–173, 2004.
[14] S. Kajita and B. Espiau, “Legged robots,” in Springer Handbook of Robotics, B. Siciliano and O. Khatib, Eds. Springer Berlin Heidelberg, 2008, pp. 361–389.
[15] J. M. Hollerbach, “Dynamic scaling of manipulator trajectories,” in American Control Conference, 1983, 1983, pp. 752–756.
[16] B. Kiss and E. Szadeczky-Kardoss, “Time-scaling in the control of mechatronic systems,” in New Developments in Robotics Automation and Control, ser. 978-953-7619-20-6, A. Lazinica, Ed. InTech, 2008, ch. 22.
[17] C. Chevallereau, “Time-scaling control for an underactuated biped robot,” IEEE Transactions on Robotics and Automation, vol. 19, no. 2, pp. 362 –368, 2003.
[18] S. Alfayad, “Robot humanoïde hydroïd: Actionnement, structure cinématique et stratègie de contrôle,” Ph.D. dissertation, Université de Versailles Saint Quentin en Yvelines, 2009.
[19] C. Rengifo, Y. Aoustin, F. Plestan, and C. Chevallereau, “Contac forces computation in a 3D bipedal robot using constrained-based and penalty-based approaches,” in International Conference on Multibody Dynamics, Brusselles,
Belgium, 2011.
[20] C. Kanzow and H. Kleinmichel, “A new class of semismooth newton-type methods for nonlinear complementarity problems,” Computational Optimization and Applications, vol. 11, no. 3, pp. 227 – 251, December 1998.
[21] M. C. Ferris, C. Kanzow, and T. S. Munson, “Feasible descent algorithms for mixed complementarity problems,” Mathematical Programming, vol. 86, no. 3, pp. 475–497, 1999.
[22] W. Khalil and E. Dombre, Modeling, Identification and Control of Robots, 2nd ed., ser. Kogan Page Science. Paris, France: Butterworth - Heinemann, 2004.
[23] S. N. Whittlesey, R. E. van Emmerik, and J. Hamill, “The swing phase of human walking is not a passive movement,” Motor Control, vol. 4, no. 3, pp. 273–292, 2000.
[24] T. Flash, Y. Meirovitch, and A. Barliya, “Models of human movement: Trajectory planning and inverse kinematics studies,” Robotics and Autonomous Systems, vol. 61, no. 4, pp. 330 – 339, 2013.
[25] R. A. Clark, Y.-H. Pua, K. Fortin, C. Ritchie, K. E. Webster, L. Denehy, and A. L. Bryant, “Validity of the microsoft kinect for assessment of postural control,” Gait & Posture, vol. 36, no. 3, pp. 372 – 377, 2012.
[26] D. Webster and O. Celik, “Experimental evaluation of microsoft kinect’s accuracy and capture rate for stroke rehabilitation applications,” in Haptics Symposium (HAPTICS), 2014 IEEE, Feb 2014, pp. 455–460.
[27] B. Sun, X. Liu, X. Wu, and H. Wang, “Human gait modeling and gait analysis based on kinect,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on, June 2014, pp. 3173–3178.