Skip to main content
Log in

Two-dimensional team lifting prediction with floating-base box dynamics and grasping force coupling

  • Published:
Multibody System Dynamics Aims and scope Submit manuscript

Abstract

An optimization-based multibody dynamics modeling method is proposed for two-dimensional (2D) team lifting prediction. The box itself is modeled as a floating-base rigid body in Denavit–Hartenberg representation. The interactions between humans and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. An inverse-dynamics-based optimization is used to simulate the team lifting motion where the dynamic effort of two humans is minimized subjected to physical and task-based constraints. The design variables are control points of cubic B-splines of joint angle profiles of two humans and the box, and the grasping forces between humans and the box. Analytical sensitivities are derived for all constraints and objective functions including the varying unknown grasping forces. Two numerical examples are successfully simulated: one is to lift a 10 kg box with the center of mass (COM) in the middle, and the other is the same weight box with the COM off the center. The humans’ joint angle, torque, ground reaction force, and grasping force profiles are reported. Reasonable team lifting motion, kinematics, and kinetics are predicted using the proposed multibody dynamic modeling approach and optimization formulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Marras, W.S., Davis, K.G., Kirking, B.C., Granata, K.P.: Spine loading and trunk kinematics during team lifting. Ergonomics 42(10), 1258–1273 (1999)

    Google Scholar 

  2. Charney, W., Zimmerman, K., Walara, E.: The lifting team: a design method to reduce lost time back injury in nursing. AAOHN J. 35, 231–234 (1991)

    Google Scholar 

  3. Daynard, D., Yassi, A., Cooper, J.E., Tate, R., Norman, R., Wells, R.: Biomechanical analysis of peak and cumulative spinal loads during simulated patient handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers. Appl. Ergon. 32, 199–214 (2001)

    Google Scholar 

  4. Sharp, M.A., Rice, V.J., Nindl, B.C., Williamson, T.L.: Effects of team size on the maximum weight bar lifting strength of military personnel. Hum. Factors 39(3), 481–488 (1997)

    Google Scholar 

  5. Cheng, F.T., Orin, D.E.: Efficient formulation of the force distribution equations for simple closed-chain robotic mechanisms. IEEE Trans. Syst. Man Cybern. 21(1), 25–32 (1991)

    Google Scholar 

  6. Lawitzky, M., Mörtl, A., Hirche, S.: Load sharing in human–robot cooperative manipulation. In: Proceedings of IEEE Ro-Man, pp. 185–191 (2010)

    Google Scholar 

  7. DelPreto, J., Rus, D.: Sharing the load: human–robot team lifting using muscle activity. In: 2019 IEEE International Conference on Robotics and Automation (ICRA), May 20–24, Montreal, Canada (2019)

    Google Scholar 

  8. Karwowski, W.: Maximum load lifting capacity of males and females in teamwork. In: Proceedings of the Human Factors Society 32nd Annual Meeting, pp. 680–682. Human Factors Society, Santa Monica (1988)

    Google Scholar 

  9. Lee, K.S., Lee, J.H.: A study of efficiency of two-man lifting work. Int. J. Ind. Ergon. 28(3–4), 197–202 (2001)

    Google Scholar 

  10. Rice, V.J., Sharp, M.A., Nindl, B.C., Bills, R.K.: Prediction of two-person team lifting capacity. In: Proceedings of the Human Factors and Ergonomics 39th Annual Meeting, pp. 645–649. Human Factors Society, Santa Monica (1995)

    Google Scholar 

  11. Fox, R.R.: A psychophysical study of bimanual lifting. Master’s thesis, Texas Tech, Lubbock, TX (1982)

  12. Mital, A., Motorwala, A.: An ergonomic evaluation of steel and composite access covers. Int. J. Ind. Ergon. 15(4), 285–296 (1995)

    Google Scholar 

  13. Karwowski, W., Mital, A.: Isometric and isokinetic testing of lifting strength of males in teamwork. Ergonomics 29(7), 869–878 (1986)

    Google Scholar 

  14. Karwowski, W., Pongpatanasuegsa, N.: Testing of isometric and isokinetic lifting strengths of untrained females in teamwork. Ergonomics 31(3), 291–301 (1988)

    Google Scholar 

  15. Johnson, S., Lewis, D.: A psychophysical study of two-person manual materials handling tasks. In: Proceedings of the Human Factors Society 33rd Annual Meeting, pp. 651–653. Human Factors Society, Santa Monica (1989)

    Google Scholar 

  16. Dennis, G.J., Barrett, R.S.: Spinal loads during two-person team lifting: effect of matched versus unmatched standing height. Int. J. Ind. Ergon. 32(1), 25–38 (2003)

    Google Scholar 

  17. Ayoub, M.: Problems and solutions in manual materials handling: the state-of-the-art. Ergonomics 35(7–8), 713–728 (1992)

    Google Scholar 

  18. Arisumi, H., Chardonnet, J.R., Kheddar, A., Yokoi, K.: Dynamic lifting motion of humanoid robots. In: 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, pp. 2661–2667 (2007)

    Google Scholar 

  19. Xiang, Y., Arora, J.S., Abdel-Malek, K.: Physics-based modeling and simulation of human walking: a review of optimization-based and other approaches. Struct. Multidiscip. Optim. 42(1), 1–23 (2010)

    MathSciNet  MATH  Google Scholar 

  20. Xiang, Y., Arora, J.S., Rahmatalla, S., Marler, T., Bhatt, R., Abdel-Malek, K.: Human lifting simulation using a multi-objective optimization approach. Multibody Syst. Dyn. 23(4), 431–451 (2010)

    MathSciNet  MATH  Google Scholar 

  21. Xiang, Y., Arora, J.S., Abdel-Malek, K.: 3D human lifting motion prediction with different performance measures. Int. J. Humanoid Robot. 9(02), 1250012 (2012)

    Google Scholar 

  22. Song, J., Qu, X., Chen, C.H.: Simulation of lifting motions using a novel multi-objective optimization approach. Int. J. Ind. Ergon. 53, 37–47 (2016)

    Google Scholar 

  23. Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39(6), 1107–1115 (2006)

    Google Scholar 

  24. Shourijeh, M.S., McPhee, J.: Forward dynamic optimization of human gait simulations: a global parameterization approach. J. Comput. Nonlinear Dyn. 9(3), 031018 (2014)

    Google Scholar 

  25. Kumar, S., Renaudin, V., Aoustin, Y., Le-Carpentier, E., Combettes, C.: Model-based and experimental analysis of the symmetry in human walking in different device carrying modes. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), UTown, Singapore, June 26–29, 2016, pp. 1172–1179 (2016)

    Google Scholar 

  26. Ren, L., Jones, R.K., Howard, D.: Predictive modelling of human walking over a complete gait cycle. J. Biomech. 40(7), 1567–1574 (2007)

    Google Scholar 

  27. Xiang, Y., Arora, J.S., Rahmatalla, S., Abdel-Malek, K.: Optimization-based dynamic human walking prediction: one step formulation. Int. J. Numer. Methods Eng. 79(6), 667–695 (2009)

    MATH  Google Scholar 

  28. Farahani, S.D., Andersen, M.S., de Zee, M., Rasmussen, J.: Optimization-based dynamic prediction of kinematic and kinetic patterns for a human vertical jump from a squatting position. Multibody Syst. Dyn. 36(1), 37–65 (2016)

    MathSciNet  Google Scholar 

  29. Ackermann, M., Van den Bogert, A.J.: Optimality principles for model-based prediction of human gait. J. Biomech. 43(6), 1055–1060 (2010)

    Google Scholar 

  30. Arora, J., Wang, Q.: Review of formulations for structural and mechanical system optimization. Struct. Multidiscip. Optim. 30(4), 251–272 (2005)

    MathSciNet  MATH  Google Scholar 

  31. Björkenstam, S., Leyendecker, S., Linn, J., Carlson, J.S., Lennartson, B.: Inverse dynamics for discrete geometric mechanics of multibody systems with application to direct optimal control. J. Comput. Nonlinear Dyn. 13(10), 101001 (2018)

    Google Scholar 

  32. Leyendecker, S., Ober-Blöbaum, S., Marsden, J.E., Ortiz, M.: Discrete mechanics and optimal control for constrained systems. Optim. Control Appl. Methods 31(6), 505–528 (2010)

    MathSciNet  MATH  Google Scholar 

  33. Roller, M., Björkenstam, S., Linn, J., Leyendecker, S.: Optimal control of a biomechanical multibody model for the dynamic simulation of working tasks. In: ECCOMAS Thematic Conference on Multibody Dynamics, Prague, Czech Republic, June 19–22, pp. 817–826 (2017)

    Google Scholar 

  34. Cheng, H., Obergefell, L., Rizer, A.: Generator of body (GEBOD) manual, AL/CF-TR-1994-0051, Armstrong Laboratory, Wright-Patterson Air Force Base, Ohio (1994)

  35. Denavit, J., Hartenberg, R.S.: A kinematic notation for lower-pair mechanisms based on matrices. J. Appl. Mech. 22, 215–221 (1955)

    MathSciNet  MATH  Google Scholar 

  36. Xiang, Y., Arora, J.S., Abdel-Malek, K.: Optimization-based motion prediction of mechanical systems: sensitivity analysis. Struct. Multidiscip. Optim. 37(6), 595–608 (2009)

    MathSciNet  MATH  Google Scholar 

  37. Fregly, B.J., Reinbolt, J.A., Rooney, K.L., Mitchell, K.H., Chmielewski, T.L.: Design of patient-specific gait modifications for knee osteoarthritis rehabilitation. IEEE Trans. Biomed. Eng. 54(9), 1687–1695 (2007)

    Google Scholar 

  38. Xiang, Y., Zaman, R., Rakshit, R., Yang, J.: Subject-specific strength percentile determination for two-dimensional symmetric lifting considering dynamic joint strength. Multibody Syst. Dyn. 46(1), 63–76 (2019)

    MathSciNet  Google Scholar 

  39. Gill, P.E., Murray, W., Saunders, M.A.: SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM J. Optim. 12(4), 979–1006 (2002)

    MathSciNet  MATH  Google Scholar 

  40. Lee, S.H., Kim, J., Park, F.C., Kim, M., Bobrow, J.E.: Newton-type algorithm for dynamics-based robot movement optimization. IEEE Trans. Robot. 21(4), 657–667 (2005)

    Google Scholar 

  41. Aguilar, L.M., Torres, J.P., Jimenes, C.R., Cabrera, D.R., Cárdenas, M.F., Urgirles, P.F.: Analysis of the angles in hip, knee and ankle during the pedaling of a Cross Country Olympic cyclist. In: 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), October 28–30 Santiago, Chile, pp. 205–208 (2015)

    Google Scholar 

  42. Xiang, Y., Chung, H.J., Kim, J.H., Bhatt, R., Rahmatalla, S., Yang, J., Marler, T., Arora, J.S., Abdel-Malek, K.: Predictive dynamics: an optimization-based novel approach for human motion simulation. Struct. Multidiscip. Optim. 41(3), 465–479 (2010)

    MathSciNet  MATH  Google Scholar 

  43. Xiang, Y.: An inverse dynamics optimization formulation with recursive B-spline derivatives and partition of unity contacts: demonstration using two-dimensional musculoskeletal arm and gait. J. Biomech. Eng. 141(3), 034503 (2019)

    Google Scholar 

  44. Xiang, Y., Arefeen, A.: Computational methods for skeletal muscle strain injury: a review. Crit. Rev. Biomed. Eng. 47(4), 277–294 (2019)

    Google Scholar 

Download references

Acknowledgements

Funding for this research was partially supported by Grant No. T42OH008421 from the National Institute for Occupational Safety and Health (NIOSH) / Centers for Disease Control and Prevention (CDC) to the Southwest Center for Occupational and Environmental Health (SWCOEH), an NIOSH Education and Research Center.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yujiang Xiang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: ZMP and GRF

Appendix: ZMP and GRF

In this study, an active–passive algorithm [27] is used to calculate ZMP and GRF to obtain the real joint torque for the multibody human system. The algorithm is outlined here as follows:

  1. (1)

    Given the state variables \(q_{i}, \dot{{q}}_{i}, \ddot{{q}}_{i}\) (design variables) for each DOF, the global resultant active forces (\(\mathbf{M}^{o}, \mathbf{F}^{o}\)) at the origin in the inertial reference frame (Fig. 1) are obtained from equations of motion without GRF using inverse dynamics.

  2. (2)

    After that, the ZMP position is calculated from its definition using the global resultant active force as follows:

    $$ y_{zmp} =0;\quad x_{zmp} =0;\qquad z_{zmp} = \frac{-M_{x}^{o}}{F_{y}^{o}}, $$
    (33)

    where \(\mathbf{M}^{o} = [ M_{x}^{o} \ \ 0\ \ 0 ]^{\mathrm{T}}\) and \(\mathbf{F}^{o} = [0\ \ F_{y}^{o}\ \ F_{z}^{o} ]^{\mathrm{T}}\). In addition, the two feet are assumed on the level ground.

  3. (3)

    After obtaining the ZMP position, the resultant active forces at ZMP (\(\mathbf{M}^{zmp}, \mathbf{F}^{zmp}\)) are computed using the equilibrium condition as follows:

    $$\begin{aligned} &\mathbf{M}^{zmp} = \mathbf{M}^{o} + \mathbf{F}^{o} \times {}^{o} \mathbf{r}_{zmp},\\ &\mathbf{F}^{zmp} = \mathbf{F}^{o}, \end{aligned}$$
    (34)

    where \({}^{o} \mathbf{r}_{zmp}\) is the ZMP position in the global coordinate system obtained from Eq. (33).

  4. (4)

    Then the value and location of GRF are calculated from the equilibrium between the resultant active forces and passive forces at the ZMP:

    $$\begin{aligned} &\mathbf{M}^{GRF} + \mathbf{M}^{zmp} = \mathbf{0}, \\ &\mathbf{F}^{GRF} + \mathbf{F}^{zmp} = \mathbf{0}, \\ &{}^{o} \mathbf{r}_{GRF} - {}^{o} \mathbf{r}_{zmp} = \mathbf{0}, \end{aligned}$$
    (35)

    where \(\mathbf{M}^{GRF} = [ M_{x}^{GRF} \ \ 0\ \ 0 ]^{\mathrm{T}}\) and \(\mathbf{F}^{GRF} = [0\ \ F_{y}^{GRF}\ \ F_{z}^{GRF} ]^{\mathrm{T}}\).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, Y., Arefeen, A. Two-dimensional team lifting prediction with floating-base box dynamics and grasping force coupling. Multibody Syst Dyn 50, 211–231 (2020). https://doi.org/10.1007/s11044-020-09742-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11044-020-09742-0

Keywords

Navigation