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Humanoid robot upper body motion generation using B-spline-based functions

Published online by Cambridge University Press:  10 March 2014

M. Ruchanurucks
M. Ruchanurucks*
Affiliation:
Electrical Engineering, Kasetsart University, Bangkok, Thailand
*
*Corresponding author. E-mail: fengmtr@ku.ac.th
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Summary

This paper aims to represent a human's upper body motion using a humanoid robot. As a robot has different physical limitations than a human, we present a method that can filter the trajectories to meet the limitations. The filtering can be used directly and also can be used as a constraint for optimization. It will be shown to be applicable both offline and online. Many physical attributes, namely angle, collision, velocity, and dynamic torque, are represented as B-spline explicit functions. The B-spline coefficients are then calculated to limit the physical attributes. This explicit method can guarantee that many limits are met. Hence, it is better than many methods that only reduce such physical attributes using objective functions.

Keywords

B-splineMotion generationRobot dynamicsRobot kinematics
Type
Articles
Information
Robotica , Volume 33 , Issue 4 , May 2015 , pp. 705 - 720
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Pollard, N. S., Hodgins, J. K., Riley, M. J. and Atkeson, C. G., “Adapting Human Motion for the Control of a Humanoid Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC (May 11–15, 2002).Google Scholar
2. Safonova, A., Pollard, N. S. and Hodgins, J. K., “Optimizing Human Motion for the Control of a Humanoid Robot,” Proceedings of the 2nd International Symposium on Adaptive Motion of Animals and Machines, Kyoto, Japan (Mar. 4–8, 2003).Google Scholar
3. Hammam, G. B. and Orin, D. E., “Whole-body humanoid control from upper-body task specifications,” Patent Application Number: 20110066283, Publication date: Mar. 17, 2011.Google Scholar
4. Lee, J. and Shin, S. Y., “A Hierarchical Approach to Interactive Motion Editing for Human-Like Figures,” Proceedings of ACM SIGGRAPH, Los Angeles, CA (Aug. 8–9, 1999) pp. 39–48.Google Scholar
5. Ude, A., Atkeson, C. G. and Riley, M., “Programming full body movements for humanoid robot by observation,” Robot. Auton. Syst. 47, 93–108 (2004).Google Scholar
6. Luo, J. and Hauser, K., “Interactive Generation of Dynamically Feasible Robot Trajectories From Sketches using Temporal Mimicking,” Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul, Minnesota (May 14–18, 2012).Google Scholar
7. Moulard, T., Yoshida, E. and Nakaoka, S., “Optimization-Based Motion Retargeting Integrating Spatial and Dynamic Constraints,” Proceedings of the 44th Symposium on Robotics, Seoul, Korea (Oct. 24–26, 2013).Google Scholar
8. Khoury, A. E., Lamiraux, F. and Taix, M., “Optimal Motion Planning for Humanoid Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (May 6–10, 2013).Google Scholar
9. Riley, M., Ude, A., Wade, K. and Atkeson, C. G., “Enabling Real-Time Full Body Imitation: A Natural Way of Transferring Human Movements to Humanoids,” Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 14–19, 2003).Google Scholar
10. Shin, S. Y. and Kim, C. H., “On-Line Human Motion Transition and Control for Humanoid Upper Body Manipulation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (Oct. 18–22, 2010).Google Scholar
11. D'Souza, A., Vijayakumar, S. and Schaal, S., “Learning Inverse Kinematics,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii (Oct. 29–Nov. 3, 2001).Google Scholar
12. Erez, T., Lowrey, K., Tassa, Y., Kumar, V., Kolev, S. and Todorov, E., “An Integrated System for Real-Time Model-Predictive Control of Humanoid Robots,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Atlanta, Georgia (Oct. 15–17, 2013).Google Scholar
13. Ruchanurucks, M., Nakaoka, S., Kudoh, S. and Ikeuchi, K., “Generation of Humanoid Robot Motions with Physical Constraints using Hierarchical B-Spline,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada (Aug. 2–6, 2005).Google Scholar
14. Nakaoka, S., Nakazawa, A., Kanehiro, F., Kaneko, K., Morisawa, M. and Ikeuchi, K., “Task Model of Lower Body Motion for a Biped Humanoid Robot to Imitate Human Dances,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada (Aug. 2–6, 2005).Google Scholar
15. Nakaoka, S. and Komura, T., “Interaction Mesh Based Motion Adaptation for Biped Humanoid Robots,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Osaka, Japan (Nov. 29–Dec. 1, 2012).Google Scholar
16. Dalibard, S., Nakhaei, A., Lamiraux, F. and Laumond, J.-P., “Whole-Body Task Planning for a Humanoid Robot: A Way to Integrate Collision Avoidance,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Paris, France (Dec. 7–10, 2009).Google Scholar
17. Seto, F., Kosuge, K. and Hirata, Y., “Self-Collision Avoidance Motion Control for Human Robot Cooperation System Using RoBE,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada (Aug. 2–6, 2005).Google Scholar
18. Zlajpah, L. and Nemec, B., “Kinematic Control Algorithms for On-Line Obstacle Avoidance for Redundant Manipulators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (Sep. 30–Oct. 4, 2002).Google Scholar
19. Ruchanurucks, M., Nakaoka, S., Kudoh, S. and Ikeuchi, K., “Humanoid Robot Motion Generation with Sequential Physical Constraints,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, Florida (May 15–19, 2006).Google Scholar
20. Korb, W. and Troch, I., “Data reduction for manipulator path planning,” Robotica 21, 605–614 (2003).CrossRefGoogle Scholar
21. Ferch, M., Zhang, J. and Knoll, A., “Robot Skill Transfer Based on B-Spline Fuzzy Controllers for Force-Control Tasks,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, Michigan (May 10–15, 1999).Google Scholar
22. Cohen, E., Daubechies, I. and Feauveau, J. C., “Biorthogonal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 45, 485–560 (1992).Google Scholar
23. Gotler, S. J., “Hierarchical and Variational Geometric Modeling with Wavelets,” Proceedings of the ACM Symposium on Interactive 3D Graphics, Monterey, USA (Apr. 9–12, 1995) pp. 35–42.Google Scholar
24. Ude, A., Atkeson, C. G. and Riley, M., “Planning of Joint Trajectories for Humanoid Robots using B-Spline Wavelets,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA (Apr. 24–28, 2000).Google Scholar
25. Shin, K. G. and McKay, N. D., “Minimum-time control of robotic manipulators with geometric path constraints,” IEEE Trans. Autom. Control 30 (6), 531–541 (1985).Google Scholar
26. Slotine, J.-J. E. and Yang, H. S., “Improving the efficiency of time-optimal path-following algorithms,” IEEE Trans. Robot. Autom. 5 (1), 118–124 (1989).Google Scholar
27. Chui, C. K. and Quak, E., “An Introduction to Wavelets,” In: Wavelet Analysis and its Application, vol. 1 (Chui, C. K., ed.) (Academic Press, Waltham, MA, 1992).Google Scholar
28. Forsey, D. and Bartels, R., “Hierarchical B-Spline Refinement,” Proceedings of the 15th Annual Conference on Computer Graphics and Interactive Techniques (1988).Google Scholar
29. Rose, C., Guenter, B., Bodenheimer, B. and Cohen, M. F., “Efficient Generation of Motion Transitions Using Space-Time Constraints,” Proceedings of ACM SIGGRAPH, New York (1996).Google Scholar
30. Huyer, W. and Neumaier, A., “Global optimization by multilevel coordinate search,” J. Glo. Optim. 14, 331–355 (1999).CrossRefGoogle Scholar
31. Lee, D. and Spong, M. W., “Passive Bilateral Teleoperation with Constant Time Delays,” Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA (Aug. 4–9, 1996).Google Scholar
32. Li, T.-Y. and Latombe, J.-C., “On-Line Manipulation Planning for Two Robot Arms in a Dynamic Environment,” Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan (May 21–27, 1995).Google Scholar
33. Mahvash, M. and Okumura, A. M., “Friction Compensation for a Force-Feedback Telerobotic System,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, Florida (May 15–19, 2006).Google Scholar