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Modeling and base parameters identification of legged robots

Published online by Cambridge University Press:  18 October 2021

Xu Chang Open the ORCID record for Xu Chang [Opens in a new window] ,
Honglei An  and
Hongxu Ma
Xu Chang
Affiliation:
Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
Honglei An*
Affiliation:
Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
Hongxu Ma
Affiliation:
Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

This paper first uses a decoupling modeling method to model legged robots. The method groups all the degrees of freedom according to the number of limbs, regarding each limb as a manipulator with serial structure, which greatly reduces the number of dynamic parameters that need to be identified simultaneously. On this basis, a step-by-step identification method from the end-effector link to the base link, referred to as “E-B” identification method, is proposed. Simulation verification is carried out on a quadruped robot with 16 degrees of freedom in Gazebo, and the validity of the method proposed is discussed.

Keywords

legged robotsdecoupling modelingidentificationdynamicsbase parameters
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 747 - 761
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Grzelczyk, D. and Awrejcewicz, J., “Modeling and control of an eight-legged walking robot driven by different gait generators,” Int. J. Struct. Stab. Dyn. 19(5), 1–23 (2019).CrossRefGoogle Scholar
Barasuol, V., Buchli, J., Semini, C., Frigerio, M., De Pieri, E. R. and Caldwell, D. G., “A Reactive Controller Framework for Quadrupedal Locomotion on Challenging Terrain,” IEEE International Conference on Robotics and Automation (2013) pp. 2554–2561.Google Scholar
Heim, S. W., Ajallooeian, M., Eckert, P., Vespignani, M. and Ijspeert, A. J., “On designing an active tail for legged robots: Simplifying control via decoupling of control objectives,” Ind. Robot Int. J. 43(3), 338–346 (2016).CrossRefGoogle Scholar
Gautier, M. and Khalil, W., “A Direct Determination of Minimum Inertial Parameters of Robots,” IEEE International Conference on Robotics and Automation (1988) pp. 1682–1687.Google Scholar
Gautier, M. and Khalil, W., “Identification of the Minimum Inertial Parameters of Robots,” Proceedings International Conference on Robotics and Automation (1989) pp. 1529–1534.Google Scholar
Gautier, M. and Khalil, W., “Direct calculation of minimum set of inertial parameters of serial robots,” IEEE Trans. Rob. Autom. 6(3), 368–373 (1990).CrossRefGoogle Scholar
Narendra, K. S. and Parthasarathy, K., “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks 1(1), 4–27 (1990).10.1109/72.80202CrossRefGoogle ScholarPubMed
Jiang, Z. H., Ishida, T. and Sunawada, M., “Neural Network Aided Dynamic Parameter Identification of Robot Manipulators,” IEEE International Conference on Systems, Man and Cybernetics (2006) pp. 3299–3303.Google Scholar
Khalil, W., Gautier, M. and Lemoine, P., “Identification of the Payload Inertial Parameters of Industrial Manipulators,” IEEE International Conference on Robotics and Automation (2007) pp. 4943–4948.Google Scholar
Wu, J., Wang, J. S. and You, Z., “An overview of dynamic parameter identification of robots,” Rob. Comput. Integr. Manuf. 26(5), 414–419 (2010).CrossRefGoogle Scholar
Neubauer, M., Gattringer, H. and Bremer, H., “A persistent method for parameter identification of a seven-axes manipulator,” Robotica 33(5), 1099–1112 (2015).CrossRefGoogle Scholar
Verdel, D., Bastide, S., Vignais, N., Bruneau, O. and Berret, B., “An identification-based method improving the transparency of a robotic upper limb exoskeleton,” Robotica 39(9), 1–18 (2021).CrossRefGoogle Scholar
Gautier, M., “Numerical calculation of the base inertial parameters of robots,” J. Field Rob. 8(4), 485–506 (1991).Google Scholar
Serban, R. and Freeman, J. S., “Identification and identifiability of unknown parameters in multibody dynamic systems,” Multibody Syst. Dyn. 5(4), 335–350 (2001).10.1023/A:1011434711375CrossRefGoogle Scholar
Diaz-Rodriguez, M., Mata, V., Farhat, N. and Provenzano, S., “Identifiability of the dynamic parameters of a class of parallel robots in the presence of measurement noise and modeling discrepancy,” Mech. Based Des. Struct. Mach. 36(4), 478–498 (2008).10.1080/15397730802446501CrossRefGoogle Scholar
Ros, J., Plaza, A., Iriarte, X. and Aginaga, J., “Inertia transfer concept based general method for the determination of the base inertial parameters,” Multibody Syst. Dyn. 34(4), 327–347 (2015).CrossRefGoogle Scholar
Iriarte, X., Ros, J., Mata, V. and Aginaga, J., “Determination of the symbolic base inertial parameters of planar mechanisms,” Eur. J. Mech. A Solids 61, 82–91 (2016).10.1016/j.euromechsol.2016.08.012CrossRefGoogle Scholar
Sauer, T., Numerical Analysis (Pearson, New York, 2012).Google Scholar
Gautier, M. and Khalil, W., “Exciting trajectories for the identification of base inertial parameters of robots,” Int. J. Rob. Res. 11(4), 362–375 (1992).CrossRefGoogle Scholar
Swevers, J., Ganseman, C., De Schutter, J. and Brussel, H. V., “Experimental robot identification using optimised periodic trajectories,” Mech. Syst. Signal Process. 10(5), 561–577 (1996).CrossRefGoogle Scholar
Swevers, J., Ganseman, C., Tükel, D. B., De Schutter, J. and Brussel, H. V., “Optimal robot excitation and identification,” IEEE Trans. Rob. Autom. 13(5), 730–740 (1997).CrossRefGoogle Scholar
Park, K.-J., “Fourier-based optimal excitation trajectories for the dynamic identification of robots,” Robotica 24(5), 625–633 (2006).10.1017/S0263574706002712CrossRefGoogle Scholar
Sousa, C. D. and Cortesão, R., “Physical feasibility of robot base inertial parameter identification: A linear matrix inequality approach,” Int. J. Rob. Res. 33(6), 931–944 (2014).CrossRefGoogle Scholar
Vantilt, J., Aertbelien, E., Groote, F. D. and Schutter, J. D., “Optimal Excitation and Identification of the Dynamic Model of Robotic Systems with Compliant Actuators,” IEEE International Conference on Robotics and Automation (2015) pp. 2117–2124.Google Scholar
Bascetta, L., Ferretti, G. and Scaglioni, B.. “ Closed form Newton–Euler dynamic model of flexible manipulators,” Robotica 35(5), 1006–1030 (2015).10.1017/S0263574715000934CrossRefGoogle Scholar
Biswal, P. and Mohanty, P. K., “Modeling and effective foot force distribution for the legs of a quadruped robot,” Robotica 39(8), 1–14 (2021).10.1017/S0263574720001307CrossRefGoogle Scholar
Khalil, W. and Dombre, E., Modeling, Identification & Control of Robots (Hermes Penton, London, 2002).Google Scholar
Horn, R. A. and Johnson, C. R., Matrix Analysis (Cambridge University Press, Cambridge, UK, 2012).CrossRefGoogle Scholar
Mayeda, H., Yoshida, K. and Osuka, K., “Base parameters of manipulator dynamic models,” IEEE Trans. Rob. Autom. 6(3), 312–321 (1990).CrossRefGoogle Scholar
Lurie, A. I., Analytical Mechanics (Springer Verlag, New York, 2002).CrossRefGoogle Scholar