Abstract
Soft robotics is an area where the robots are designed by using soft and compliant modules which provide them with infinite degrees of freedom. The intrinsic movements and deformation of such robots are complex, continuous and highly compliant because of which the current modelling techniques are unable to predict and capture their dynamics. This paper describes a machine learning based actuation and system identification technique to discover the governing dynamics of a soft bodied swallowing robot. A neural based generator designed by using Matsuoka’s oscillator has been implemented to actuate the robot so that it can deliver its maximum potential. The parameters of the oscillator were found by defining and optimising a quadratic objective function. By using optical motion tracking, time-series data was captured and stored. Further, the data were processed and utilised to model the dynamics of the robot by assuming that few significant non-linearities are governing it. It has also been shown that the method can generalise the surface deformation of the time-varying actuation of the robot.
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References
Yap, H.K., Ng, H.Y., Yeow, C.-H.: High-force soft printable pneumatics for soft robotic applications. Soft Robot. 3(3), 144–158 (2016)
Katzschmann, R.K., Marchese, A.D., Rus, D.: Hydraulic autonomous soft robotic fish for 3d swimming. In: Proceedings Conference on Experimental Robotics, pp. 405–420. Springer (2016)
Ranzani, T., Cianchetti, M., Gerboni, G., Falco, D., Menciassi, A.: A soft modular manipulator for minimally invasive surgery: Design and characterization of a single module. 32(1), 187–200 (2016)
Song, Y.S., Sun, Y., Van Den Brand, R., Von Zitzewitz, J., Micera, S., Courtine, G., Paik, J.: Soft robot for gait rehabilitation of spinalized rodents. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013 Conference Proceedings. pp. 971–976. IEEE (2013)
Wei, Y., Chen, Y., Ren, T., Chen, Q., Yan, C., Yang, Y., Li, Y.: A novel, variable stiffness robotic gripper based on integrated soft actuating and particle jamming. Soft Robot. 3(3), 134–143 (2016)
Chen, F.J., Dirven, S., Xu, W.L., Li, X.N.: Soft actuator mimicking human esophageal peristalsis for a swallowing robot. IEEE/ASME Trans. Mechatron. 19(4), 1300–1308 (2014)
Zhu, M., Xie, M., Xu, W., Cheng, L.K.: A nanocomposite-based stretchable deformation sensor matrix for a soft-bodied swallowing robot. IEEE Sens. J. 16(10), 3848–3855 (2016)
Rus, D., Tolley, M.T.: Design, fabrication and control of soft robots. Nature 521(7553), 467–475 (2015). https://doi.org/10.1038/nature14543
Dirven, S., Chen, F., Xu, W., Bronlund, J.E., Allen, J., Cheng, L.K.: Design and characterization of a peristaltic actuator inspired by esophageal swallowing. IEEE/ASME Trans. Mechatron. 19(4), 1234–1242 (2014)
Dirven, S., Xu, W., Cheng, L.K.: Sinusoidal peristaltic waves in soft actuator for mimicry of esophageal swallowing. IEEE/ASME Trans. Mechatron. 20(3), 1331–1337 (2015)
Bhattacharya, D., Nisha, M.G., Pillai, G.: Relevance vector-machine-based solar cell model. Int. J. Sustain. Energy 34(10), 685–692 (2015). https://doi.org/10.1080/14786451.2014.885030
Iplikci, S.: Support vector machines–based generalized predictive control. Int. J. Robust Nonlinear Control 16(17), 843–862 (2006). https://doi.org/10.1002/rnc.1094
Matsuoka, K.: Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biol. Cybern. 52(6), 367–376 (1985). https://doi.org/10.1007/BF00449593
Fang, Y., Hu, J., Liu, W., Chen, B., Qi, J., Ye, X.: A cpg-based online trajectory planning method for industrial manipulators. In: Conference Proceedings 2016 Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 41–46 (2016)
Bhattacharya, D., Cheng, L.K., Dirven, S., Xu, W: Actuation planning and modeling of a soft swallowing robot. In: Mechatronics and Machine Vision in Practice (M2VIP), 2017 24th International Conference on. pp. 1–6. IEEE (2017)
Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. 113(15), 3932–3937 (2016)
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The work presented in this paper was funded by Riddet Institute Centre of Research Excellence, New Zealand.
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Bhattacharya, D., Cheng, L.K., Dirven, S., Xu, W. (2019). Artificial Intelligence Approach to the Trajectory Generation and Dynamics of a Soft Robotic Swallowing Simulator. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_1
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DOI: https://doi.org/10.1007/978-3-319-78452-6_1
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