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Dynamics and path tracking of continuum robotic arms using data-driven identification tools

Published online by Cambridge University Press:  09 August 2021

Aida Parvaresh Open the ORCID record for Aida Parvaresh [Opens in a new window]  and
S. Ali A. Moosavian
Aida Parvaresh
Affiliation:
Center of Excellence in Robotics and Control, Advanced Robotics & Automated systems (ARAS) Laboratory, Dept of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
S. Ali A. Moosavian*
Affiliation:
Center of Excellence in Robotics and Control, Advanced Robotics & Automated systems (ARAS) Laboratory, Dept of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: moosavian@kntu.ac.ir
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Abstract

In this paper, forward/inverse dynamics of a continuum robotic arm is developed using a data-driven approach, which could tackle uncertainties and extreme nonlinearities to obtain reliable solutions. By establishing a direct mapping between the actuator and task spaces, the unnecessary mappings of actuator-to-configuration then configuration-to-task are eliminated, to reduce extra computational cost. The proposed approach is validated through simulation (based on Cosserat rod theory) and experimental tests on RoboArm. Next, path tracking in the presence/absence of obstacles as well as load carrying maneuver are investigated. Finally, the obtained results concerning repeatability, scalability, and disturbance rejection performance of the approach are discussed.

Keywords

Continuum robotic armpath trackingforward dynamics probleminverse dynamics problemsystem identificationload carrying maneuver
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1098 - 1124
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Saab, W., Rone, W. S., and Ben-Tzvi, P., “Robotic tails: A state-of-the-art review,” Robotica. 36(9), 1263–1277 (2018).CrossRefGoogle Scholar
Li, J. and Xiao, J., “An efficient algorithm for real time collision detection involving a continuum manipulator with multiple uniform-curvature sections,” Robotica. 34(7), 1566–1586 (2016).CrossRefGoogle Scholar
Xu, K., Zhao, J., and Zheng, X., “Configuration comparison among kinematically optimized continuum manipulators for robotic surgeries through a single access port,” Robotica. 33(10), 2025–2044 (2015).CrossRefGoogle Scholar
Braganza, D., Dawson, D. M., Walker, I. D., and Nath, N., “Neural network grasping controller for continuum robots,” Proceedings of the 45th IEEE Conference on Decision and Control, (2006) pp. 6445–6449.Google Scholar
Cobos-Guzman, S., Palmer, D., and Axinte, D., “Kinematic model to control the end-effector of a continuum robot for multi-axis processing,” Robotica. 35(1), 224–240 (2017).CrossRefGoogle Scholar
Thuruthel, T. G., Falotico, E., Renda, F., and Laschi, C., “Learning dynamic models for open loop predictive control of soft robotic manipulators,” Bioinspir. Biomim. 12(6), 66003 (2017).CrossRefGoogle ScholarPubMed
Bamdad, M. and Bahri, M. M., “Kinematics and manipulability analysis of a highly articulated soft robotic manipulator,” Robotica. 37(5), 868–882 (2019).CrossRefGoogle Scholar
Neppalli, S., Csencsits, M. A., Jones, B. A., and Walker, I. D., “Closed-form inverse kinematics for continuum manipulators,” Adv. Robot. 23(15), 2077–2091 (2009).CrossRefGoogle Scholar
Falkenhahn, V., Mahl, T., Hildebrandt, A., Neumann, R., and Sawodny, O., “Dynamic modeling of bellows-actuated continuum robots using the Euler–Lagrange formalism,” IEEE Trans. Robot. 31(6), 1483–1496 (2015).CrossRefGoogle Scholar
Chikhaoui, M. T., Lilge, S., Kleinschmidt, S., and Burgner-Kahrs, J., “Comparison of modeling approaches for a tendon actuated continuum robot with three extensible segments,” IEEE Robot. Autom. Lett. 4(2), 989–996 (2019).CrossRefGoogle Scholar
Peyron, Q., Boehler, Q., Rabenorosoa, K., Nelson, B. J., Renaud, P., and Andreff, N., “Kinematic analysis of magnetic continuum robots using continuation method and bifurcation analysis,” IEEE Robot. Autom. Lett. 3(4), 3646–3653 (2018).CrossRefGoogle Scholar
Kang, R., Branson, D. T., Zheng, T., Guglielmino, E., and Caldwell, D. G., “Design, modeling and control of a pneumatically actuated manipulator inspired by biological continuum structures,” Bioinspir. Biomim. 8(3), 36008 (2013).CrossRefGoogle ScholarPubMed
Habibi, H., Yang, C., Godage, I. S., Kang, R., Walker, I. D., and Branson, D. T., “A lumped-mass model for large deformation continuum surfaces actuated by continuum robotic arms,” J. Mech. Robot. 12(1), 011014 (2020).CrossRefGoogle Scholar
Kim, Y., Cheng, S. S., Diakite, M., Gullapalli, R. P., Simard, J. M., and Desai, J. P., “Toward the development of a flexible mesoscale MRI-compatible neurosurgical continuum robot,” IEEE Trans. Robot. 33(6), 1386–1397 (2017).CrossRefGoogle ScholarPubMed
Dehghani, M. and Moosavian, S. A. A., “Compact modeling of spatial continuum robotic arms towards real-time control,” Adv. Robot. 28(1), 15–26 (2014).CrossRefGoogle Scholar
Dehghani, M. and Moosavian, S. A. A., “Modeling and control of a planar continuum robot,” Advanced Intelligent Mechatronics (AIM), 2011 IEEE/ASME International Conference on (2011) pp. 966–971.Google Scholar
Solomatine, D., See, L. M., and Abrahart, R. J., “Data-driven modelling: concepts, approaches and experiences,” In: Practical hydroinformatics, edited by R. J. Abrahart (Springer-Verlag Berlin Heidelberg, Germany, 2009) pp. 17–30.Google Scholar
Li, M., Kang, R., Branson, D. T., and Dai, J. S., “Model-free control for continuum robots based on an adaptive Kalman filter,” IEEE/ASME Trans. Mechatronics. 23(1), 286–297 (2017).CrossRefGoogle Scholar
Tan, N., Gu, X., and Ren, H., “Pose characterization and analysis of soft continuum robots with modeling uncertainties based on interval arithmetic,” IEEE Trans. Autom. Sci. Eng. 16(2), 570–584 (2018).CrossRefGoogle Scholar
Jakes, D., Ge, Z., and Wu, L., “Model-less Active Compliance for Continuum Robots using Recurrent Neural Networks,” arXiv Prepr. arXiv1902.08943 (2019).CrossRefGoogle Scholar
Melingui, A., Merzouki, R., Mbede, J. B., Escande, C., Daachi, B., and Benoudjit, N., “Qualitative approach for inverse kinematic modeling of a compact bionic handling assistant trunk,” in Neural Networks (IJCNN), 2014 International Joint Conference on (2014) pp. 754–761.Google Scholar
D’Souza, A., Vijayakumar, S., and Schaal, S., “Learning inverse kinematics,” Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180) (2001) pp. 298–303.Google 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 (2013).Google Scholar
Vannucci, L., Falotico, E., Di Lecce, N., Dario, P., and Laschi, C., “Integrating feedback and predictive control in a bio-inspired model of visual pursuit implemented on a humanoid robot,” Conference on Biomimetic and Biohybrid Systems (2015) pp. 256–267.Google Scholar
Thuruthel, T. G., Shih, B., Laschi, C., and Tolley, M. T., “Soft robot perception using embedded soft sensors and recurrent neural networks,” Sci. Robot. 4(26), 1–9 (2019).CrossRefGoogle ScholarPubMed
Grassmann, R., Modes, V., and Burgner-Kahrs, J., “Learning the forward and inverse kinematics of a 6-DOF concentric tube continuum robot in SE (3),” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 5125–5132.Google Scholar
Parvaresh, A., Moosavi, S. A., and Moosavian, S. A. A., “Identification and Position Control of a Continuum Robotic Arm,” 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM) (2017) pp. 310–315.Google Scholar
Parvaresh, A. and Moosavian, S. A. A., “Modeling and Model-free Fuzzy Control of a Continuum Robotic Arm,” 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM) (2018) pp. 501–506.Google Scholar
Parvaresh, A. and Moosavian, S. A. A., “Experimental identification and hybrid PID-fuzzy position control of continuum robotic arms,” Int. J. Robot. Theory Appl. 6(1), 53–63 (2020).Google Scholar
Nelles, O., Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes (Springer-Verlag Berlin Heidelberg GmbH, Germany, 2020).CrossRefGoogle Scholar
Ouyang, B., Liu, Y., Tam, H.-Y., and Sun, D., “Design of an interactive control system for a multisection continuum robot,” IEEE/ASME Trans. Mechatronics. 23(5), 2379–2389 (2018).CrossRefGoogle Scholar
Alambeigi, F., Wang, Z., Liu, Y.-H., Taylor, R. H., and Armand, M., “A versatile data-driven framework for model-independent control of continuum manipulators interacting with obstructed environments with unknown geometry and stiffness,” arXiv Prepr. arXiv2005.01951 (2020).Google Scholar
Dehghani, M., “Dynamics modeling and control for continuum robot,” K. N. Toosi University of Technology (2014).Google Scholar
Gravagne, I. A., Rahn, C. D., and Walker, I. D., “Large deflection dynamics and control for planar continuum robots,” IEEE/ASME Trans. Mechatronics. 8(2), 299–307 (2003).CrossRefGoogle Scholar
Thuruthel, T. G., Manti, M., Falotico, E., Cianchetti, M., and Laschi, C., “Induced Vibrations of Soft Robotic Manipulators for Controller Design and Stiffness Estimation,” 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) (2018) pp. 550–555.Google Scholar
Yamaguchi, T. et al., “A mechanical approach to suppress the oscillation of a long continuum robot flying with water jets,” IEEE Robot. Autom. Lett. 4(4), 4346–4353 (2019).CrossRefGoogle Scholar
Gao, G., Li, L., Wang, H., and Du, Y., “Study on Vibration Analyze and Control of Continuum Robot,” 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (2018) pp. 675–679.Google Scholar
Penning, R. S. and Zinn, M. R., “A combined modal-joint space control approach for continuum manipulators,” Adv. Robot. 28(16), 1091–1108 (2014).CrossRefGoogle Scholar
Li, M., Kang, R., Geng, S., and Guglielmino, E., “Design and control of a tendon-driven continuum robot,” Trans. Inst. Meas. Control. 40(11), 3263–3272 (2018).CrossRefGoogle Scholar
George Thuruthel, T., Falotico, E., Manti, M., Pratesi, A., Cianchetti, M., and Laschi, C., “Learning closed loop kinematic controllers for continuum manipulators in unstructured environments,” Soft Robot. 4(3), 285–296 (2017).CrossRefGoogle ScholarPubMed
Renda, F., Giorelli, M., Calisti, M., Cianchetti, M., and Laschi, C., “Dynamic model of a multibending soft robot arm driven by cables,” IEEE Trans. Robot. 30(5), 1109–1122 (2014).CrossRefGoogle Scholar
Renda, F., Boyer, F., Dias, J., and Seneviratne, L., “Discrete cosserat approach for multisection soft manipulator dynamics,” IEEE Trans. Robot. 34(6), 1518–1533 (2018).CrossRefGoogle Scholar
Trivedi, D., Lotfi, A., and Rahn, C. D., “Geometrically exact dynamic models for soft robotic manipulators,” 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (2007) pp. 1497–1502.Google Scholar