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Planning load-effective dynamic motions of highly articulated human model for generic tasks

Published online by Cambridge University Press:  20 October 2008

Joo H. Kim ,
Jingzhou Yang  and
Karim Abdel-Malek
Joo H. Kim*
Affiliation:
U.S. Army Virtual Soldier Research Program, Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242, USA.
Jingzhou Yang
Affiliation:
Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Karim Abdel-Malek
Affiliation:
U.S. Army Virtual Soldier Research Program, Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242, USA.
*
*Corresponding author. E-mail: jookim@engineering.uiowa.edu
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Summary

The robotic motion planning criteria has evolved from kinematics to dynamics in recent years. Many research achievements have been made in dynamic motion planning, but the externally applied loads are usually limited to the gravity force. Due to the increasing demand for generic tasks, the motion should be generated for various functions such as pulling, pushing, twisting, and bending. In this paper, a comprehensive form of equations of motion, which includes the general external loads applied at any point of branched tree structures, is implemented. An optimization-based algorithm is then developed to generate load-effective motions of redundant tree-structured systems for generic tasks. A highly articulated dual-arm human model is used to generate different effective motions to sustain different external load magnitudes. The results also provide a new scientific insight of human motion.

Keywords

Motion planningEquation of motionLoad-effectiveOptimizationRedundantGeneral external loadTree structure

Information

Type
Article
Information
Robotica , Volume 27 , Issue 5 , September 2009 , pp. 739 - 747
Copyright
Copyright © Cambridge University Press 2008

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References

1. Sciavicco, L. and Siciliano, B., “A solution algorithm to the inverse kinematic problem for redundant manipulators,” IEEE J. Rob. Automat. 4 (4), 403–410 (Aug. 1988).CrossRefGoogle Scholar
2. Piazzi, A. and Visioli, A., “Global minimum-jerk trajectory planning of robot manipulators,” IEEE Trans. Indus. Elec. 47 (1), 140–149 (Feb. 2000).CrossRefGoogle Scholar
3. Nikravesh, P. E., Computer-Aided Analysis of Mechanical Systems (Prentice-Hall, Englewood Cliffs, New Jersey, 1988).Google Scholar
4. Haug, E. J., Computer-Aided Kinematics and Dynamics of Mechanical Systems (Allyn and Bacon, Boston, 1989).Google Scholar
5. Jain, A. and Rodriguez, G., “Recursive flexible multibody system dynamics using spatial operators,” J. Guidance, Control Dyn. 15 (6), 1453–1466 (1992).CrossRefGoogle Scholar
6. Saha, S. K., “A decomposition of the manipulator inertia matrix,” IEEE Trans. Rob. Automat. 13 (2), 301–304 (1997).CrossRefGoogle Scholar
7. Anderson, K. S. and Duan, S., “A hybrid parallelizable low order algorithm for dynamics of multi-rigid-body systems: Part I, chain systems,” J. Math. Comput. Model. 30, 193–215 (1999).CrossRefGoogle Scholar
8. Anderson, K. S. and Duan, S., “Highly parallelizable low order dynamics algorithm for complex multi-rigid-body systems,” AIAA J. Guidance, Control Dyn. 23 (2), 355–364 (2000).CrossRefGoogle Scholar
9. Blajer, W., “On the determination of joint reactions in multibody mechanisms,” J. Mech. Des. 126 (2), 341–350 (Mar. 2004).CrossRefGoogle Scholar
10. Hemami, H. and Wyman, B. F., “Rigid body dynamics, constraints, and inverses,” J. Appl. Mech. 74 (1), 47–56 (Jan. 2007).CrossRefGoogle Scholar
11. Lee, K., Wang, Y. and Chirikjian, G. S., “O(n) mass-matrix inversion for serial manipulators and polypeptide chains using Lie derivatives,” Robotica 25 (6), 729–750 (2007).CrossRefGoogle Scholar
12. Suh, K. and Hollerbach, J., “Local Versus Global Torque Optimization of Redundant Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation, Raleigh, NC, USA, Vol. 4 (Mar. 1987) pp. 619–624.Google Scholar
13. Gompertz, R. S. and Yang, D. C. H., “Feasibility Evaluation of Dynamically Linearized Kinematically Redundant Planar Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation, Philadelphia, PA, USA, Vol. 1 (Apr. 1988) pp. 178–182.Google Scholar
14. Nakamura, Y., Advanced Robotics: Redundancy and Optimization, Boston, MA, USA (Addison-Wesley, 1991).Google Scholar
15. Nedungadi, A. and Kazerouinian, K., “A local solution with global characteristics for the joint torque optimization of a redundant manipulator,” J. Rob. Syst. 6 (5), 631–654 (1989).CrossRefGoogle Scholar
16. Chung, Y. S., Griffis, M. and Duffy, J., “Unique joint displacement generation for redundant robotic systems,” ASME Rob., Spatial Mech. Mech. Syst. 45, 637–641 (1992).Google Scholar
17. Chung, C. Y., Lee, B. H., Kim, M. S. and Lee, C. W., “Torque optimizing control with singularity-robustness for kinematically redundant robots,” J. Intell. Rob. Syst. 28 (3), 231–258 (Jul. 2000).CrossRefGoogle Scholar
18. Shiller, Z. and Dubowsky, S., “On computing the global time-optimal motions of robotic manipulators in the presence of obstacles,” IEEE Trans. Rob. Automat. 7 (6), 785–797 (Dec. 1991).CrossRefGoogle Scholar
19. Shiller, Z., “Optimal robot motion planning and work-cell layout design,” Robotica 15 (1), 31–40 (Jan. 1997).CrossRefGoogle Scholar
20. Singh, S. K. and Leu, M. C., “Manipulator motion planning in the presence of obstacles and dynamic constraints,” Int. J. Rob. Res. 10 (2), 171–187 (1991).CrossRefGoogle Scholar
21. Wang, C.-Y. E., Timoszyk, W. K. and Bobrow, J. E., “Payload maximization for open chained manipulators: finding weightlifting motions for a Puma 762 robot,” IEEE Trans. Rob. Automat. 17 (2), 218–224 (Apr. 2001).CrossRefGoogle Scholar
22. Saramago, S. F. P. and Ceccarelli, M., “An optimum robot path planning with payload constraints,” Robotica 20 (4), 395–404 (2002).CrossRefGoogle Scholar
23. Bobrow, J. E., Park, F. C. and Sideris, A., “Recent Advances on the Algorithmic Optimization of Robot Motion,” In: Fast Motions in Biomechanics and Robotics, Lecture Notes in Control and Information Sciences, Vol. 340 (Springer, Berlin/Heidelberg, 2006) pp. 21–41.CrossRefGoogle Scholar
24. Vukobratovic, M. and Kircanski, M., “A dynamic approach to nominal trajectory synthesis for redundant manipulators,” IEEE Trans. Syst., Man, Cybernet. SMC-14, 580–586 (Jul.–Aug. 1984).CrossRefGoogle Scholar
25. Hirakawa, A. R. and Kawamura, A., “Trajectory generation for redundant manipulators under optimization of consumed electrical energy,” IEEE Industry Applications Conference, Thirty-First IAS Annual Meeting, San Diego, CA, USA, Vol. 3 (Oct. 1996) pp. 1626–1632.Google Scholar
26. Nokleby, S. B. and Podhorodeski, R. P., “Pose optimization of serial manipulators using knowledge of their velocity-degenerate (singular) configurations,” J. Rob. Syst. 20 (5), 239–249 (Apr. 2003).CrossRefGoogle Scholar
27. Papadopoulos, E. and Gonthier, Y., “On Manipulator Posture Planning for Large Force Tasks,” Proceedings of IEEE International Conference on Robotics and Automation, Nagoya, Japan, Vol. 1 (May 1995) pp. 126–131.Google Scholar
28. Papadopoulos, E. and Gonthier, Y., “A framework for large-force task planning of mobile and redundant manipulators,” J. Rob. Syst. 16 (3), 151–162 (Feb. 1999).3.0.CO;2-7>CrossRefGoogle Scholar
29. McLean, S. G., Su, A. and van den Bogert, A. J., “Development and validation of a 3-D model to predict knee joint loading during dynamic movement,” J. Biomech. Eng. 125 (6), 864–874 (2003).CrossRefGoogle ScholarPubMed
30. Blajer, W. and Czaplicki, A., “An alternative scheme for determination of joint reaction forces in human multibody models,” J. Theor. Appl. Mech. 43 (4), 813–824 (2005).Google Scholar
31. Hirashima, M., Kudo, K. and Ohtsuki, T., “A new non-orthogonal decomposition method to determine effective torques for three-dimensional joint rotation,” J. Biomech. 40 (4), 871–882 (2007).CrossRefGoogle ScholarPubMed
32. Jung, E. S., Kee, D. and Chung, M. K., “Upper body reach posture prediction for ergonomic evaluation models,” Int. J. Indus. Ergonom. 16, 95–107 (1995).CrossRefGoogle Scholar
33. Hase, K. and Yamazaki, N., “Development of three-dimensional whole-body musculoskeletal model for various motion analyses,” JSME Int. J., Series C 40 (1), 25–32 (Mar. 1997).Google Scholar
34. Anderson, F. C. and Pandy, M. G., “Dynamic optimization of human walking,” J. Biomech. Eng. 123 (5), 381–390 (2001).CrossRefGoogle ScholarPubMed
35. Kim, J. H., Abdel-Malek, K., Yang, J. and Marler, T., “Prediction and analysis of human motion dynamics performing various tasks,” Int. J. Human Factors Model. Simul. 1 (1), 69–94 (2006).CrossRefGoogle Scholar
36. Kawasaki, H., Beniya, Y. and Kanzaki, K., “Minimum Dynamics Parameters of Tree Structure Robot Models,” Proceedings of IEEE International Conference on Industrial Electronics, Control and Instrumentation, Kobe, Japan Vol. 2 (1991) pp. 1100–1105.Google Scholar
37. Nakamura, Y. and Yamane, K., “Dynamics computation of structure-varying kinematic chains and its application to human figures,” IEEE Trans. Rob. Automat. 16 (2), 124–134 (Apr. 2000).CrossRefGoogle Scholar
38. Bobrow, J. E., Martin, B., Sohl, G., Wang, E. C., Park, F. C. and Kim, J., “Optimal robot motions for physical criteria,” J. Rob. Syst. 18 (12), 785–795 (Dec. 2001).CrossRefGoogle Scholar
39. Featherstone, R. and Orin, D., “Robot Dynamics: Equations and Algorithms,” Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, Vol. 1 (2000) pp. 826–834.Google Scholar
40. Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” J. Appl. Mech. 77, 215–221 (1955).CrossRefGoogle Scholar
41. Fu, K. S., Gonzalez, R. C. and Lee, C. S. G., Robotics: Control, Sensing, Vision, and Intelligence, New York, NY, USA (McGraw-Hill, 1987).Google Scholar
42. Anand, V. B., Computer Graphics and Geometric Modeling for Engineers (John Wiley and Sons, New York, 1993).Google Scholar
43. Arora, J. S., Introduction to Optimum Design (McGraw-Hill, New York, 1989).Google Scholar
44. Gill, P. E., Murray, W. and Saunders, M. A., “SNOPT: An SQP algorithm for large-scale constrained optimization,” SIAM J. Optim. 12, 979–1006 (2002).CrossRefGoogle Scholar
45. Yang, J., Kim, J. H., Pitarch, E. Pena and Abdel-Malek, K., “Optimal Trajectory Planning for Redundant Manipulators Based on Minimum Jerk,” ASME International Design Engineering Technical Conferences, New York City, NY (Aug., 2008).CrossRefGoogle Scholar
46. Hoffman, S. G., Reed, M. P. and Chaffin, D. B., “Predicting Force-Exertion Postures from Task Variables,” Proceedings of SAE Digital Human Modeling for Design and Engineering, Seattle, WA (Jun. 2007).CrossRefGoogle Scholar