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Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators

Published online by Cambridge University Press:  03 February 2021

A. H. Bouyom Boutchouang Open the ORCID record for A. H. Bouyom Boutchouang [Opens in a new window] ,
Achille Melingui ,
J. J. B. Mvogo Ahanda Open the ORCID record for J. J. B. Mvogo Ahanda [Opens in a new window] ,
Othman Lakhal ,
Frederic Biya Motto  and
Rochdi Merzouki
A. H. Bouyom Boutchouang*
Affiliation:
Department of Electrical and Telecommunications Engineering, Ecole Nationale Supérieure Polytechnique, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: [email protected]
Achille Melingui
Affiliation:
Department of Electrical and Telecommunications Engineering, Ecole Nationale Supérieure Polytechnique, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: [email protected]
J. J. B. Mvogo Ahanda
Affiliation:
Department of Electrical and Power Engineering, University of Bamenda, Bamenda 39 Bambili, Cameroun. E-mail: [email protected]
Othman Lakhal
Affiliation:
CRIStAL Laboratory, CNRS-UMR,Villeneuve d’Ascq 59655, France. E-mails: [email protected], [email protected]
Frederic Biya Motto
Affiliation:
Department of Physic’s, Faculty of Sciences, University of Yaounde I, Yaounde 8390, Cameroon. E-mail: [email protected]
Rochdi Merzouki
Affiliation:
CRIStAL Laboratory, CNRS-UMR,Villeneuve d’Ascq 59655, France. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

Keywords

Continuum manipulatorsForward kinematicsDeep learningAutoencoders (AE) and MLP
Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021 , pp. 1760 - 1778
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Pritts, M. B. and Rahn, C. D., “Design of an Artificial Muscle Continuum Robot,” 2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04, vol. 5 (IEEE, 2004) pp. 4742–4746.CrossRefGoogle Scholar
Walker, I. D., “Continuous Backbone ‘Continuum’ Robot Manipulators,” ISRN Robotics, vol. 2013 (2013).CrossRefGoogle Scholar
Cieślak, R. and Morecki, A., “Elephant trunk type elastic manipulator-a tool for bulk and liquid materials transportation,” Robotica 17(1), 11–16 (1999).CrossRefGoogle Scholar
Hannan, M. W. and Walker, I. D., “Kinematics and the implementation of an elephant’s trunk manipulator and other continuum style robots,” J. Field Robot. 20(2), 45–63 (2003).Google ScholarPubMed
McMahan, W., Jones, B., Walker, I., Chitrakaran, V., Seshadri, A. and Dawson, D., “Robotic Manipulators Inspired by Cephalopod Limbs,” Proceedings of CDEN Design Conference Citeseer, vol. 20(2) (2004) pp. 1–10Google Scholar
Camarillo, D. B., Carlson, C. R. and Salisbury, J. K., “Configuration tracking for continuum manipulators with coupled tendon drive,” IEEE Trans. Robot. 25(4), 798–808 (2009).CrossRefGoogle Scholar
Simaan, N., Taylor, R. and Flint, P., “A Dexterous System for Laryngeal Surgery,” IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, vol. 1 (IEEE, 2004) pp. 351–357.Google Scholar
Casper, J. and Murphy, R. R., “Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center,” IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 33(3), 367–385 (2003).CrossRefGoogle Scholar
Walker, I. D., Mattfeld, R., Mutlu, A., Bartow, A. and Giri, N., “A Novel Approach to Robotic Climbing Using Continuum Appendages in In-Situ Exploration,” 2012 IEEE Aerospace Conference (IEEE, 2012) pp. 1–9.Google Scholar
Liu, S., Yang, Z., Zhu, Z., Han, L., Zhu, X. and Xu, K., “Development of a dexterous continuum manipulator for exploration and inspection in confined spaces,” Ind. Robot. Int. J. 43(3), 284–295 (2016).CrossRefGoogle Scholar
Webster, R. J. III and Jones, B. A., “Design and kinematic modeling of constant curvature continuum robots: A review,” Int. J. Robot. Res. 29(13), 1661–1683 (2010).CrossRefGoogle Scholar
Godage, I. S., Guglielmino, E., Branson, D. T., Medrano-Cerda, G. A. and Caldwell, D. G., “Novel Modal Approach for Kinematics of Multisection Continuum Arms,” 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2011) pp. 1093–1098.Google Scholar
Godage, I. S., Branson, D. T., Guglielmino, E., Medrano-Cerda, G. A. and Caldwell, D. G., “Shape Function-Based Kinematics and Dynamics for Variable Length Continuum Robotic Arms,” 2011 IEEE International Conference on Robotics and Automation (IEEE, 2011) pp. 452–457.CrossRefGoogle Scholar
Dupont, P. E., Lock, J. and Butler, E., “Torsional Kinematic Model for Concentric Tube Robots,” 2019 IEEE International Conference on Robotics and Automation (IEEE, 2009) pp. 3851–3858.Google Scholar
Escande, C., Merzouki, R., Pathak, P. M. and Coelen, V., “Geometric Modelling of Multisection Bionic Manipulator: Experimental Validation on Robotinoxt,” 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) (IEEE, 2012) pp. 2006–2011.Google Scholar
Rolf, M. and Steil, J. J., “Constant Curvature Continuum Kinematics as Fast Approximate Model for the Bionic Handling Assistant,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2012) pp. 3440–3446.Google Scholar
Li, Z. and Du, R., “Design and analysis of a bio-inspired wire-driven multi-section flexible robot,” Int. J. Adv. Robot. Syst. 10(4), 209 (2013).CrossRefGoogle Scholar
Gravagne, I. A., Rahn, C. D. and Walker, I. D., “Large deflection dynamics and control for planar continuum robots,” IEEE/ASME Trans. Mech. 8(2), 299–307 (2003).Google Scholar
Trivedi, D., Lotfi, A. and Rahn, C. D., “Geometrically exact models for soft robotic manipulators,” IEEE Trans. Robot. 24(4), 773–780 (2008).CrossRefGoogle Scholar
Chirikjian, G. S. and Burdick, J. W., “A modal approach to hyperredundant manipulator kinematics,” IEEE Trans. Robot. Autom. 10(3), 343–354 (1994).CrossRefGoogle Scholar
Jones, B. A. and Walker, I. D., “Kinematics for multisection continuum robots,” IEEE Trans. Robot. 22(1), 43–55 (2006).CrossRefGoogle Scholar
Mahl, T., Hildebrandt, A. and Sawodny, O., “A variable curvature continuum kinematics for kinematic control of the bionic handling assistant,” IEEE Trans. Robot. 30(4), 935–949 (2014).CrossRefGoogle Scholar
Kang, R., Guglielmino, E., Branson, D. T. and Caldwell, D. G., “Kinematic Model and Inverse Control for Continuum Manipulators,” 2013 10th IEEE International Conference on Control and Automation (ICCA) (IEEE, 2013) pp. 1615–1620.Google 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,” 2014 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2014) pp. 754–761.CrossRefGoogle Scholar
Giorelli, M., Renda, F., Ferri, G. and Laschi, C., “A Feed-Forward Neural Network Learning the Inverse Kinetics of a Soft Cable-Driven Manipulator Moving in Three-Dimensional Space,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2013) pp. 5033–5039.CrossRefGoogle Scholar
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) (IEEE, 2018) pp. 5125–5132.CrossRefGoogle Scholar
He, W., Ge, S. S., Li, Y., Chew, E. and Ng, Y. S., “Neural network control of a rehabilitation robot by state and output feedback,” J. Intell. Robot. Syst. 80(1), 15–31 (2015).CrossRefGoogle Scholar
Reinhart, R., Shareef, Z. and Steil, J., “Hybrid analytical and data-driven modeling for feed-forward robot control,” Sensors 17(2), 311 (2017).CrossRefGoogle ScholarPubMed
Lakhal, O., Melingui, A. and Merzouki, R., “Hybrid approach for modeling and solving of kinematics of a compact bionic handling assistant manipulator,” IEEE/ASME Trans. Mech. 21(3), 1326–1335 (2016).Google Scholar
Bengio, Y., “Learning deep architectures for AI,” Found. Trends Mach. Learn. 2(1), 1–127 (2009).CrossRefGoogle Scholar
Bengio, Y. and LeCun, Y., “Scaling learning algorithms towards AI,” Large-Scale Kernel Mach. 34(5), 1–41 (2007).Google Scholar
Larochelle, H., Bengio, Y., Louradour, J. and Lamblin, P., “Exploring strategies for training deep neural networks,” J. Mach. Learn. Res. 10(1), 1–40 (2009).Google Scholar
Fukumizu, K. and Amari, S.-I., “Local minima and plateaus in hierarchical structures of multilayer perceptrons,” Neural Networks 13(3), 317–327 (2000).CrossRefGoogle ScholarPubMed
Larochelle, H., Erhan, D., Courville, A., Bergstra, J. and Bengio, Y., “An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation,” Proceedings of the 24th International Conference on Machine Learning (2007) pp. 473–480.Google Scholar
Salakhutdinov, R., “Learning deep generative models,” Ann. Rev. Stat. Appl. 2(1), 361–385 (2015).CrossRefGoogle Scholar
Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M. and Salem, A.-B. M., “Classification using deep learning neural networks for brain tumors,” Future Comput. Inform. J. 3(1), 68–71 (2018).CrossRefGoogle Scholar
Kamilaris, A. and Prenafeta-Bold, F. X., “Deep learning in agriculture: A survey,” Comput. Electron. Agricul. 147(1), 70–90 (2018).CrossRefGoogle Scholar
Cheng, G., Yang, C., Yao, X., Guo, L. and Han, J., “When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNS,” IEEE Trans. Geosci. Remote Sens. 56(5), 2811–2821 (2018).CrossRefGoogle Scholar
Liu, Q., Hang, R., Song, H. and Li, Z., “Learning multiscale deep features for high-resolution satellite image scene classification,” IEEE Trans. Geosci. Remote Sens. 56(1), 117–126 (2017).CrossRefGoogle Scholar
Qiu, X., Zhang, L., Ren, Y., Suganthan, P. N. and Amaratunga, G., “Ensemble Deep Learning for Regression and Time Series Forecasting,” 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) (IEEE, 2014) pp. 1–6.Google Scholar
Wang, Q., Wan, J. and Yuan, Y., “Deep metric learning for crowdedness regression,” IEEE Trans. Circ. Syst. Video Tech. 28(10), 2633–2643 (2018).CrossRefGoogle Scholar
Renda, F., Cianchetti, M., Giorelli, M., Arienti, A. and Laschi, C., “A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm,” Bioinspiration Biomimetics 7(2), 025006 (2012).CrossRefGoogle ScholarPubMed
Escande, C., Chettibi, T., Merzouki, R., Coelen, V. and Pathak, P. M., “Kinematic calibration of a multisection bionic manipulator,” IEEE/ASME Trans. Mechatronics 20(2), 663–674 (2014).CrossRefGoogle Scholar
Han, J. and Moraga, C., “The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning,” International Workshop on Artificial Neural Networks (Springer, 1995) pp. 195–201.Google Scholar
Hu, Z., Li, Y. and Yang, Z., “Improving Convolutional Neural Network Using Pseudo Derivative ReLU, “2018 5th International Conference on Systems and Informatics (ICSAI) (IEEE, 2018) pp. 283–287.CrossRefGoogle Scholar
Poli, R., Kennedy, J. and Blackwell, T., “Particle swarm optimization,” Swarm Intell. 1(1), 33–37, Springer (2007).CrossRefGoogle Scholar
Dekkers, A. and Aarts, E., “Global optimization and simulated annealing” Math. Program. 50(1–3), 367–393, Springer (1991).CrossRefGoogle Scholar
Tanese, R., Distributed Genetic Algorithms for Function Optimization (University of Michigan,1989).Google Scholar
Ethni, S. A., Zahawi, B., Giaouris, D. and Acarnley, P. P., “Comparison of Particle Swarm and Simulated Annealing Algorithms for Induction Motor Fault Identification,” 2009 7th IEEE International Conference on Industrial Informatics (IEEE, 2009) pp. 470–474.CrossRefGoogle Scholar
Savsani, V., Rao, R. V and Vakharia, D. P., “Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms,” Mech. Mach. Theory 45(3), 531–541, Elsevier (2010).CrossRefGoogle Scholar
Eberhart, R. C. and Shi, Y., “Comparison Between Genetic Algorithms and Particle Swarm Optimization,” International Conference on Evolutionary Programming (Springer, 1998) pp. 611–616.Google Scholar
Prechelt, L., Proben1: A set of neural network benchmark problems and benchmarking rules (1994).Google Scholar
OptiTrack, Motion capture systems (2018), visited on 06 December 2018. Online. Available at: https://optitrack.com/motion-capture-robotics/.Google Scholar