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Kinematic modeling and solution of rigid-flexible and variable-diameter underwater continuous manipulator with load

Published online by Cambridge University Press:  02 August 2021

Chen Yang Open the ORCID record for Chen Yang [Opens in a new window] ,
He Xu ,
Xin Li  and
Fengshu Yu
Chen Yang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
He Xu*
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
Xin Li
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
Fengshu Yu
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
*
*Corresponding author. E-mail: railway railway_dragon@sohu.com
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Abstract

This paper presents a method to solve the kinematics of a rigid-flexible and variable-diameter continuous manipulator. The multi-segment underwater manipulator is driven by McKibben water hydraulic artificial muscle (WHAM). Considering the effect of elasticity and friction, we optimized the static mathematical model of WHAM. The kinematic model of the manipulator with load is established based on the hypothesis of piecewise constant curvature (PCC). We developed an optimization algorithm to calculate the length of the WHAMs according to the principle of minimum strain energy and obtain the configuration space parameters of the kinematic model. Based on the infinitesimal method, the homogeneous transformation matrices of the variable-diameter bending sections are computed, and the terminal position and attitude are obtained. In this paper, we studied the working space of the manipulator by quantitative analysis of the impact factors including pressure and load. A deep neural network (DNN) with six hidden layers is designed to solve inverse kinematics. The forward kinematic results are used to train and test the DNN, and the correlation coefficient between the output and target samples reaches 0.945. We carried out an underwater experiment and verified the effectiveness of the kinematic modeling and solution method.

Keywords

robot kinematicssoft actuatorhydraulic driveoptimization algorithmdeep learning
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1020 - 1035
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Polygerinos, P., Correll, N., Morin, S. A., Mosadegh, B., Onal, C. D., Petersen, K., Cianchetti, M., Tolley, M. T. and Shepherd, R. F., “Soft robotics: Review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction,” Adv. Eng. Mater. 19(12), Article No. 1700016 (2017).Google Scholar
Aracri, S., Giorgio-Serchi, F., Suaria, G., Sayed, M. E., Nemitz, M. P., Mahon, S. and Stokes, A. A., “Soft robots for ocean exploration and offshore operations: A perspective,” Soft Rob. ahead of print.Google Scholar
Peng, Z. and Huang, J., “Soft rehabilitation and nursing-care robots: A review and future outlook,” Appl. Sci.-Basel 9(15) (2019).Google Scholar
Jing, Z., Qiao, L., Pan, H., Yang, Y. and Chen, W., “An overview of the configuration and manipulation of soft robotics for on-orbit servicing,” Sci. China-Inf. Sci. 60(5), 1–19 (2017).CrossRefGoogle Scholar
Webster, R. J., III and B. A. Jones, “Design and kinematic modeling of constant curvature continuum robots: A review,” Int. J. Rob. Res. 29(13), 1661–1683 (2010).CrossRefGoogle Scholar
Della Santina, C., Katzschmann, R. K., Bicchi, A., D. Rus and IEEE, “Dynamic control of soft robots interacting with the environment,” 2018 IEEE International Conference on Soft Robotics (RoboSoft) (2018) pp. 46–53.Google Scholar
Wang, H., Yang, B., Liu, Y., Chen, W., Liang, X. and Pfeifer, R., “Visual servoing of soft robot manipulator in constrained environments with an adaptive controller,” IEEE-ASME Trans. Mech. 22(1), 41–50 (2017).Google Scholar
Della Santina, C., Bicchi, A. and Rus, D., “On an improved state parametrization for soft robots with piecewise constant curvature and its use in model based control,” IEEE Rob. Autom. Lett. 5(2), 1001–1008 (2020).CrossRefGoogle Scholar
Connolly, F., Polygerinos, P., Walsh, C. J. and Bertoldi, K., “Mechanical programming of soft actuators by varying fiber angle,” Soft Rob. 2(1), 26–32 (2015).CrossRefGoogle Scholar
Hassan, T., Cianchetti, M., Moatamedi, M., Mazzolai, B., Laschi, C. and Dario, P., “Finite-element modeling and design of a pneumatic braided muscle actuator with multifunctional capabilities,” IEEE-ASME Trans. Mech. 24(1), 109–119 (2019).CrossRefGoogle Scholar
Nozaki, T. and Noritsugu, T., “Motion analysis of McKibben type pneumatic rubber artificial muscle with finite element method,” Int. J. Autom. Technol. 8(2), 147–158 (2014).CrossRefGoogle Scholar
Shiva, A., Sadati, S. M. H., Noh, Y., Fras, J., Ataka, A., Wurdemann, H., Hauser, H., Walker, I. D., Nanayakkara, T. and Althoefer, K., “Elasticity versus hyperelasticity considerations in quasistatic modeling of a soft finger-like robotic appendage for real-time position and force estimation,” Soft Rob. 6(2), 228–249 (2019).CrossRefGoogle ScholarPubMed
Stilli, A., Kolokotronis, E., Fras, J., Ataka, A., Althoefer, K. and Wurdemann, H. A., “Static Kinematics for an Antagonistically Actuated Robot Based on a Beam-Mechanics-based Model,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 6959–6964.Google Scholar
Gonthina, P. S., Kapadia, A. D., Godage, I. S., I. A. Walker and IEEE, “Modeling Variable Curvature Parallel Continuum Robots Using Euler Curves,” 2019 International Conference on Robotics and Automation (ICRA) (2019) pp. 1679–1685.Google Scholar
Alebooyeh, M. and Urbanic, R. J., “Neural network model for identifying workspace, forward and inverse kinematics of the 7-DOF YuMi 14000 ABB collaborative robot,” IFAC Papersonline 52(10), 176–181 (2019).CrossRefGoogle Scholar
Rolf, M. and Steil, J. J., “Efficient exploratory learning of inverse kinematics on a bionic elephant trunk,” IEEE Trans. Neural Networks Learn. Syst. 25(6), 1147–1160 (2014).Google Scholar
Almusawi, A. R. J., Dulger, L. C. and Kapucu, S., “A new artificial neural network approach in solving inverse kinematics of robotic arm (Denso VP6242),” Comput. Intell. Neurosci. Article No. 5720163 (2016).CrossRefGoogle Scholar
He, W., Ouyang, Y. and Hong, J., “Vibration control of a flexible robotic manipulator in the presence of input deadzone,” IEEE Trans. Ind. Inf. 13(1), 48–59 (2017).CrossRefGoogle Scholar
Cao, Z., Xiao, Q., Huang, R. and Zhou, M., “Robust neuro-optimal control of underactuated snake robots with experience replay,” IEEE Trans. Neural Networks Learn. Syst. 29(1), 208–217 (2018).CrossRefGoogle ScholarPubMed
Wang, H., Xu, H., Yu, F., Li, X., Yang, C., Chen, S., Chen, J., Zhang, Y. and Zhou, X., “Modeling and Experiments on the Swallowing and Disgorging Characteristics of an Underwater Continuum Manipulator,” 2020 IEEE International Conference on Robotics and Automation (ICRA) (2020) pp. 2946–2952.Google Scholar
Ching-Ping, C. and Hannaford, B., “Static and Dynamic Characteristics of McKibben Pneumatic Artificial Muscles,” Proceedings of the 1994 IEEE International Conference on Robotics and Automation, vol. 281 (1994) pp. 281–286.Google Scholar
Hocking, E. G. and Wereley, N. M., “Analysis of nonlinear elastic behavior in miniature pneumatic artificial muscles,” Smart Mater. Struct. 22(1), Article No. 014016 (2012).Google Scholar
Kothera, C. S., Jangid, M., Sirohi, J. and Wereley, N. M., “Experimental characterization and static modeling of McKibben actuators,” J. Mech. Des. 131(9), Article No. 091010 (2009).CrossRefGoogle Scholar
Woods, B. K. S., Kothera, C. S. and Wereley, N. M., “Wind tunnel testing of a helicopter rotor trailing edge flap actuated via pneumatic artificial muscles,” J. Intell. Mater. Syst. Struct. 22(13), 1513–1528 (2011).CrossRefGoogle Scholar
Gong, Z., Cheng, J., Chen, X., Sun, W., Fang, X., Hu, K., Xie, Z., Wang, T. and Wen, L., “A bio-inspired soft robotic arm: Kinematic modeling and hydrodynamic experiments,” J. Bionic Eng. 15(2), 204–219 (2018).CrossRefGoogle Scholar
Jones, B. A. and Walker, I. D., “Kinematics for multisection continuum robots,” IEEE Trans. Rob. 22(1), 43–55 (2006).CrossRefGoogle Scholar
Sheikhi, S., Shojaeifard, M. and Baghani, M., “Finite bending and straightening of hyperelastic materials: Analytical solution and FEM,” Int. J. Appl. Mech. 11(9) (2019).CrossRefGoogle Scholar