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Muscle force distribution for adaptive control of a humanoid robot arm with redundant bi-articular and mono-articular muscle mechanism

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Abstract

Robot arms driven by bi-articular and mono-articular muscles have numerous advantages. If one muscle is broken, the functionality of the arm is not influenced. In addition, each joint torque is distributed to numerous muscles, and thus the load of each muscle can be relatively small. This paper addresses the problem of muscle control for this kind of robot arm. A relatively mature control method (i.e. sliding mode control) was chosen to get joint torque first and then the joint torque was distributed to muscle forces. The muscle force was computed based on a Jacobian matrix between joint torque space and muscle force space. In addition, internal forces were used to optimize the computed muscle forces in the following manner: not only to make sure that each muscle force is in its force boundary, but also to make the muscles work in the middle of their working range, which is considered best in terms of fatigue. Besides, all the dynamic parameters were updated in real-time. Compared with previous work, a novel method was proposed to use prediction error to accelerate the convergence speed of parameter. We empirically evaluated our method for the case of bending-stretching movements. The results clearly illustrate the effectiveness of our method towards achieving the desired kinetic as well as load distribution characteristics.

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References

  1. Oh S, Hori Y (2009) Development of two-degree-of-freedom control for robot manipulator with biarticular muscle torque. In: American Control Conference 2009, pp 325–330

  2. Klug S, Mohl B, Stryk OV, Barth O (2005) Design and application of a 3 DOF bionic robot arm. In: 3rd International symposium on adaptive motion in animals and machines 2005, pp 1–6

  3. Potkonjak V, Jovanovic KM, Milosavljevic P, Bascarevic N, Holland O (2011) The puller-follower control concept in the multi-jointed robot body with antagonistically coupled compliant drives. In: IASTED international conference on robotics 2011, pp 375–381

  4. Tahara K, Luo Z, Arimoto S, Kino H (2005) Sensor-motor control mechanism for reaching movements of a redundant musculo-skeletal arm. J Robot Syst 22:639–651

    Article  MATH  Google Scholar 

  5. Ivancevic VG, Ivancevic TT (2005) Natural biodynamics. World Scientific, Singapore

    Book  MATH  Google Scholar 

  6. Crago PE (2000) Creating neuromusculoskeletal models. Biomechanics and neural control of posture and movement. Springer, Berlin, pp 119–133

    Book  Google Scholar 

  7. Winter DA (1990) Biomechanics and motor control of human movement. Wiley, New York

  8. Tzafestas S, Raibert M, Tzafestas C (1996) Robust sliding-mode control applied to a 5-link biped robot

  9. Kumamoto M (2006) Revolution in humanoid robotics: evolution of motion control. Tokyo Denki University Publisher, Tokyo

  10. Liegeois A (1977) Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Trans Syst Man Cybern 7:868–871

    Article  MATH  Google Scholar 

  11. Manal K, Gonzalez RV, Lloyd DG, Buchanan TS (2002) A real-time EMG-driven virtual arm. Comput Biol Med 32:25–36

    Article  Google Scholar 

  12. Pandy MG (2001) Computer modeling and simulation of human movement. Annu Rev Biomed Eng 3:245–273

    Article  Google Scholar 

  13. Tee KP, Burdet E, Chew CM, Milner TE (2004) A model of force and impedance in human arm movements. Biol Cybern 90:368–375

    Article  MATH  Google Scholar 

  14. Hogan N (1984) Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans Autom Control 29:681–690

    Article  MATH  Google Scholar 

  15. Anderson FC, Pandy MG (2001) Dynamic optimization of human walking. J Biomech Eng 125:381–390

    Article  Google Scholar 

  16. Neptune RR (1999) Optimization algorithm performance in determining optimal controls in human movement analyses. J Biomech Eng 121:249–252

    Article  Google Scholar 

  17. Crowninshield RD, Brand RA (1981) A physiologically based criterion of muscle force prediction in locomotion. J Biomech 14:793–801

    Article  Google Scholar 

  18. Glitsch U, Baumann W (1997) The three-dimensional determination of internal loads in the lower extremity. J Biomech 30:1123–1131

    Article  Google Scholar 

  19. Hogan N (1984) An organizing principle for a class of voluntary movements. J Neurosci 4:2745–2754

    Google Scholar 

  20. Patriarco AG, Mann RW, Simon SR, Mansour JM (1981) An evaluation of the approaches of optimization models in the prediction of muscle forces during human gait. J Biomech 14:513–525

    Article  Google Scholar 

  21. Anderson FC, Pandy MG (1999) A dynamic optimization solution for vertical jumping in three dimensions. Comput Methods Biomech Biomed Eng 2:201–231

    Article  Google Scholar 

  22. Damsgaard M, Rasmussen J, Christensen ST, Surma E, Zee MD (2006) Analysis of musculoskeletal systems in the Anybody modeling system. Simul Model Pract Theory 14:1100–1111

    Article  Google Scholar 

  23. Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT et al (2007) Opensim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54:1940–1950

    Article  Google Scholar 

  24. Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9:718–727

    Article  Google Scholar 

  25. Gottlieb GL (1993) A computational model of the simplest motor program. J Mot Behav 25:153–161

    Article  Google Scholar 

  26. Karniel A, Inbar GF (1996) A model for learning human reaching-movements. Biol Cybern 77:173–183

    Article  Google Scholar 

  27. Katayama M, Kawato M (1993) Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse models. Biol Cybern 69:353–362

    MATH  Google Scholar 

  28. Chaumette F, Hutchinson S (2006) Visual servo control—part I: basic approaches. IEEE Robot Autom Mag 13:82–90

    Article  Google Scholar 

  29. Yoshikawa T (1984) Analysis and control of robot manipulators with redundancy. In: Robotics research: the first international symposium. MIT Press, Cambridge, pp 735–748

  30. Maciejewski AA, Klein CA (1985) Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Int J Robot Res 4:109–117

    Article  Google Scholar 

  31. Hollerbach JM, Suh KC (1987) Redundancy resolution of manipulators through torque optimization. IEEE J Robot Autom 3:308–316

    Article  Google Scholar 

  32. Khosla PK, Kanade T (1988) Experimental evaluation of nonlinear feedback and feedforward control schemes for manipulators. Int J Robot Res 7:18–28

    Article  Google Scholar 

  33. Heim A, Stryk OV (2000) Trajectory optimization of industrial robots with application to computer-aided robotics and robot controllers. Optimization 47:407–420

    Article  MathSciNet  MATH  Google Scholar 

  34. Graupe D (1972) Identification of systems. Litton Educational Publishing, New York

  35. Zhou K, Doyle JC (1997) Essentials of robust control. Prentice Hall, New Jersey

    MATH  Google Scholar 

  36. Slotine JE, Li W (1991) Applied nonlinar control. Prentice Hall, New Jersey

  37. Brown M, Harris C (1994) Neurofuzzy adaptive modelling and control. Prentice Hall, New Jersey

    Google Scholar 

  38. Slotine JE, Li W (1988) Adaptive manipulator control: a case study. IEEE Trans Autom Control 33:995–1003

    Article  MATH  Google Scholar 

  39. Dong H, Luo Z, Nagano A (2010) Adaptive attitude control for redundant time-varying complex model of human body in the nursing activity. J Robot Mechatron 22:418–429

    Google Scholar 

  40. Nagano A, Yoshioka S, Komura T, Himeno R, Fukashiro S (2005) A three-dimensional linked segment model of the whole human body. Int J Sport Health Sci 3:311–325

    Article  Google Scholar 

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Authors and Affiliations

  1. New York University AD, P.O. Box 129188, Abu Dhabi, UAE

    Haiwei Dong & Nikolaos Mavridis

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  1. Haiwei Dong
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  2. Nikolaos Mavridis
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Correspondence to Haiwei Dong.

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Dong, H., Mavridis, N. Muscle force distribution for adaptive control of a humanoid robot arm with redundant bi-articular and mono-articular muscle mechanism. Artif Life Robotics 18, 41–51 (2013). https://doi.org/10.1007/s10015-013-0097-x

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  • DOI: https://doi.org/10.1007/s10015-013-0097-x

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