Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-26T14:46:15.888Z Has data issue: false hasContentIssue false

Imitation of human motion for humanoid robot in lift and carry event

Published online by Cambridge University Press:  03 December 2019

Shu-Yin Chiang
Affiliation:
Department of Information and Telecommunications Engineering, Ming Chuan University, 5 De Ming Road, Gui Shan District, Taoyuan City 333, Taiwan; e-mail: sychiang@mail.mcu.edu.tw
Hao-Ge Jiang
Affiliation:
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore; e-mail: haoge001@ntu.edu.sg

Abstract

This study proposed a method to enable a humanoid robot to step up onto a stair by imitating the step-up motion of a human and to accomplish a lift and carry event in HuroCup of Federation of International RoboSports Association. The step-up motion, divided into five states, was captured by a Kinect sensor, and the human joints corresponded to the humanoid robot joints. Selected servomotors and their angle variation were matched with that of human joint numbers by a designed fuzzy inference system on the basis between the human and the humanoid robot joints. Then, the rest of the robot motors were adjusted by the zero moment point obtained from force-sensing registers to maintain stability. Next, two intermediate transition states were added between each state of the humanoid robot step-up to maintain its balance and reduce motor damage. Finally, to be applied in a real lift and carry event, a vision system was integrated to recognize the edge of a color board and determine a suitable site for the step-up. With these functions integrated, the robot under the proposed method was verified to successfully achieve the task of the lift and carry event without losing its balance or falling.

Type
Robot Athletes and Entertainers
Copyright
© Cambridge University Press, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Betin, F., Pinchon, D. & Capolino, G.-A. 2000. Fuzzy logic applied to speed control of a stepping motor drive. IEEE Transactions on Industrial Electronics 47, 610622, Jun 2000.CrossRefGoogle Scholar
Calderon, C. A. & Hu, H. 2005. Robot imitation from human body movements. In Proceedings of the third International Symposium on Imitation in Animals and Artifacts, 19.Google Scholar
Chiang, S.-Y., Kuo, S.-C., Lin, J.-B. & Chen, C.-H. 2017. Dynamic imitation of human motion for humanoid robot. In IEEE Smart World Congress, DOI: 10.1109/UIC-ATC.2017.8397437.CrossRefGoogle Scholar
Chien, D. V., Sung, K. J. & Kim, J.-W. 2015. Sensory reflex control of a humanoid robot using FSR sensor. In IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 14061409.Google Scholar
Da, X., Hartley, R. & Grizzle, J. W. 2017. Supervised learning for stabilizing underactuated bipedal robot locomotion, with outdoor experiments on the wave field. In IEEE International Conference on Robotics and Automation (ICRA), 34763483.Google Scholar
El-Iaithy, R. A., Huang, J. & Yeh, M. 2012. Study on the use of Microsoft Kinect for robotics applications. In IEEE Position Location and Navigation Symposium, 12801288.Google Scholar
Field, M., Pan, Z., Stirling, D. & Naghdy, F. 2011. Human motion capture sensors and analysis in robotics. Industrial Robot: An International Journal 38, 163171.CrossRefGoogle Scholar
Fu, C. & Chen, K. 2008. Gait synthesis and sensory control of stair climbing for a humanoid robot. IEEE Transactions on Industrial Electronics 55, 21112120.Google Scholar
Gutmann, J.-S., Fukuchi, M. & Fujita, M. 2004. Stair climbing for humanoid robots using stereo vision. In Proceedings 2004 international Conference on Intelligent Robots and Systems, 14071413.Google Scholar
Ha, I., Tamura, Y. & Asama, H. 2013. Development of open platform humanoid robot DARwIn-OP. In SICE Annual Conference, 21782181.Google Scholar
Koenemann, J., Burget, F. & Bennewitz, M. 2014. Real-time imitation of human whole-body motions by humanoids. In 2014 IEEE International Conference on Robotics & Automation (ICRA), 28062812.Google Scholar
Nakazawa, A., Nakaoka, S., Ikeuchi, K. & Yokoi, K. 2002. Imitating human dance motions through motion structure analysis. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems 3, 25392544.CrossRefGoogle Scholar
Nguyen, V. V. & Lee, J.-H. 2012. Full-body imitation of human motions with Kinect and heterogeneous kinematic structure of humanoid robot. In Proceedings of IEEE/SICE International Symposium on System Integration, 9398.Google Scholar
Poubel, L. P., Sakka, S., Ćehajić, D. & Creusot, D. 2014. Support changes during online human motion imitation by a humanoid robot using task specification. In IEEE International Conference on Robotics & Automation (ICRA), 17821787.Google Scholar
Williams, R. L. II. 2012. DARwIn-OP humanoid robot kinematics. ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 4, 11871196.Google Scholar
Zhao, J. & Bose, B. K. 2002. Evaluation of membership functions for fuzzy logic controlled induction motor drive. In IEEE Annual Conference of Industrial Electronics Society, 229234.Google Scholar
Zheng, X., Fu, M., Yang, Y. & Lv, N. 2012. 3D human postures recognition using Kinect. In Proceedings of the 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, 344347.Google Scholar