Abstract
This paper presents an optimization-based approach to estimate the hydrodynamic parameters namely drag and added mass coefficients from free-running experiments conducted on an in-house developed Anguilliform-inspired robot. The objective of the optimization problem is to estimate the hydrodynamic parameters that minimize the differences between the trajectories obtained from the simulations and the physical experiments when operated for identical gait parameters and controller gains for both the straight and the turning motions. The hydrodynamic parameters obtained from the developed approach leads to a maximum root-mean-square (RMS) position error of 0.183 BL and a maximum RMS velocity error of 0.03 BL/s between the trajectories obtained from simulations and experiments. Experimental results suggest that the parameters estimated using the developed approach can be useful in predicting the robot’s motion accurately. Accurate robot motion prediction is the fundamental requirement for localization, collision prediction, and motion planning algorithms which in turn are required for automated inspection, maintenance, and repair of sub-sea structures using Anguilliform-inspired robots.
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Kelasidi, E., Liljeback, P., Pettersen, K.Y., Gravdahl, J.T.: Innovation in underwater robots: Biologically inspired swimming snake robots. IEEE Robot. Autom. Mag. 23(1), 44–62 (2016). https://doi.org/10.1109/MRA.2015.2506121
Raj, A., Thakur, A.: Dynamically feasible trajectory planning for anguilliform-inspired robots in the presence of steady ambient flow. Robot. Auton. Syst. 118, 144–158 (2019)
Hollinger, G.A., Pereira, A.A., Binney, J., Somers, T., Sukhatme, G.S.: Learning uncertainty in ocean current predictions for safe and reliable navigation of underwater vehicles. J. Field Robot. 33(1), 47–66 (2015). https://doi.org/10.1002/rob.21613
Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica 33(3), 463–497 (2015)
Pereira, A.A., Binney, J., Hollinger, G.A., Sukhatme, G.S.: Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. J. Field Robot. 30(5), 741–762 (2013). https://doi.org/10.1002/rob.21472
Jardine, P.T., Givigi, S.: A predictive motion planner for guidance of autonomous UAV systems. In: 2016 Annual IEEE Systems Conference (SysCon), pp. 1–6. https://doi.org/10.1109/SYSCON.2016.7490514(2016)
Hegde, R., Panagou, D.: Multi-agent motion planning and coordination in polygonal environments using vector fields and model predictive control. In: 2016 European Control Conference (ECC), pp. 1856–1861. https://doi.org/10.1109/ECC.2016.7810561 (2016)
Howard, T., Pivtoraiko, M., Knepper, R.A., Kelly, A.: Model-predictive motion planning: Several key developments for autonomous mobile robots. IEEE Robot. Autom. Mag. 21(1), 64–73 (2014). https://doi.org/10.1109/MRA.2013.2294914
Kelasidi, E., Pettersen, K.Y., Gravdahl, J.T., Liljebäck, P.: Modeling of underwater snake robots. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4540–4547. https://doi.org/10.1109/ICRA.2014.6907522 (2014)
Suebsaiprom, P., Lin, C.L., Engkaninan, A.: Undulatory locomotion and effective propulsion for fish-inspired robot. Control Eng. Pract. 58, 66 – 77 (2017). https://doi.org/10.1016/j.conengprac.2016.09, http://www.sciencedirect.com/science/article/pii/S096706611630199X
Scaradozzi, D., Palmieri, G., Costa, D., Pinelli, A.: BCF swimming locomotion for autonomous underwater robots: a review and a novel solution to improve control and efficiency, vol. 130. https://doi.org/10.1016/j.oceaneng.2016.11.055, http://www.sciencedirect.com/science/article/pii/S0029801816305613 (2017)
Verma, S., Xu, J.X.: Data-assisted modeling and speed control of a robotic fish. IEEE Trans. Ind. Electron. 64(5), 4150–4157 (2017). https://doi.org/10.1109/TIE.2016.2613500
Ozmen Koca, G., Bal, C., Korkmaz, D., Bingol, M.C., Ay, M., Akpolat, Z.H., Yetkin, S.: Three-dimensional modeling of a robotic fish based on real carp locomotion. Applied Sciences 8(2), 180 (2018)
Gus’kova, N., Makhortykh, G.V., Shcheglova, M.G.: Inertia and drag of elliptic cylinders oscillating in a fluid. Fluid Dyn. 33(1), 91–95 (1998). https://doi.org/10.1007/BF02698165
Paidoussis, M.P.: 8 solitary cylindrical structures in axial flow. In: Slender Structures and Axial Flow, Fluid-Structure Interactions, vol. 2, pp. 787–1032. Academic Press. https://doi.org/10.1016/S1874-5652(04)80004-8, http://www.sciencedirect.com/science/article/pii/S1874565204800048 (2003)
Kelasidi, E., Pettersen, K.Y., Gravdahl, J.T.: A waypoint guidance strategy for underwater snake robots. In: 2014 22nd Mediterranean Conference of Control and Automation (MED), pp. 1512–1519, IEEE (2014)
Raj, A., Thakur, A.: Fish-inspired robots: design, sensing, actuation, and autonomy—a review of research. Bioinspir. Biom. 11(3), 031001 (2016)
Epps, B., y Alvarado, P., Youcef-Toumi, K., Techet, A.: Swimming performance of a biomimetic compliant fish-like robot. Exper. Fluids 47(6), 927–939 (2009). https://doi.org/10.1007/s00348-009-0684-8
Guan, Z., Gu, N., Gao, W., Nahavandi, S.: 3D hydrodynamic analysis of a biomimetic robot fish. In: 2010 11th International Conference on Control Automation Robotics & Vision, pp. 793–798. https://doi.org/10.1109/ICARCV.2010.5707359 (2010)
Lighthill, M.J.: Note on the swimming of slender fish. J. Fluid Mech. 9(2), 305–317 (1960). https://doi.org/10.1017/S0022112060001110
Lighthill, M.: Aquatic animal propulsion of high hydromechanical efficiency. J. Fluid Mech. 44(02), 265–301 (1970)
Lighthill, M.: Large-amplitude elongated-body theory of fish locomotion. Proc. R. Soc. Lond. Ser. B Biol. Sci. 179(1055), 125–138 (1971)
Morison, J., Johnson, J., Schaaf, S.: The force exerted by surface waves on piles. J. Petrol. Technol. 2 (05), 149–154 (1950). https://doi.org/10.2118/950149-G
Boyer, F., Porez, M., Leroyer, A., Visonneau, M.: Fast dynamics of an eel-like robot - comparisons with navier stokes simulations. IEEE Trans. Robot. 24(6), 1274–1288 (2008). https://doi.org/10.1109/TRO.2008.2006249
Boyer, F., Porez, M., Leroyer, A.: Poincaré–cosserat equations for the lighthill three-dimensional large amplitude elongated body theory: Application to robotics. J. Nonlinear Sci. 20(1), 47–79 (2010)
Ramananarivo, S., Godoy-Diana, R., Thiria, B.: Passive elastic mechanism to mimic fish-muscle action in anguilliform swimming. J. R. Soc. Interface 10(88) (2013). https://doi.org/10.1098/rsif.2013.0667
Porez, M., Boyer, F., Ijspeert, A., et al.: Improved lighthill fish swimming model for bio-inspired robots-modelling, computational aspects and experimental comparisons. Int. J. Robot. Res. 33(10), 1322–1341 (2014)
Clark, A.J., Tan, X., McKinley, P.K.: Evolutionary multiobjective design of a flexible caudal fin for robotic fish. Bioinspir. Biom. 10(6), 065006 (2015) [http://stacks.iop.org/1748-3190/10/i=6/a=065006]
Wang, W., Zhao, J., Xiong, W., Cao, F., Xie, G.: Underwater Electric Current Communication of Robotic Fish: Design and Experimental Results. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1166–1171. IEEE (2015)
Wang, J., McKinley, P.K., Tan, X.: Dynamic modeling of robotic fish with a flexible caudal fin. In: ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference, American Society of Mechanical Engineers, pp. 203–212 (2012)
Khalil, W., Rongere, F.: Dynamic modeling of floating systems: Application to eel-like robot and rowing system. In: 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC), pp. 21–30. IEEE (2014)
Apneseth, C.C., Day, A.H., Clelland, D.: Hydrodynamics of an oscillating articulated eel-like structure. Ocean Eng. 37(13), 1221–1232 (2010). https://doi.org/10.1016/j.oceaneng.2010.06.001
Kelasidi, E., Pettersen, K.Y., Gravdahl, J.T., et al.: Modeling and Propulsion Methods of Underwater Snake Robots. In: 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 819–826. IEEE (2017)
Kopman, V., Laut, J., Acquaviva, F., Rizzo, A., Porfiri, M.: Dynamic modeling of a robotic fish propelled by a compliant tail. IEEE J. Ocean. Eng. 40(1), 209–221 (2015). https://doi.org/10.1109/JOE.2013.2294891
McIsaac, K.A., Ostrowski, J.P.: Motion planning for anguilliform locomotion. IEEE Trans. Robot. Autom. 19(4), 637–652 (2003). https://doi.org/10.1109/TRA.2003.814495
Zuo, Z., Wang, Z., Li, B., Ma, S.: Serpentine locomotion of a snake-like robot in water environment. In: 2008 IEEE International Conference on Robotics and Biomimetics, pp 25–30. https://doi.org/10.1109/ROBIO.2009.4912974(2009)
Nguyen, P.L., Lee, B.R., Ahn, K.K.: Thrust and swimming speed analysis of fish robot with non-uniform flexible tail. J. Bion. Eng. 13(1), 73–83 (2016). https://doi.org/10.1016/S1672-6529(14)60161-X, http://www.sciencedirect.com/science/article/pii/S167265291460161X
Kanso, E., Marsden, J.E., Rowley, C.W., Melli-Huber, J.B.: Locomotion of articulated bodies in a perfect fluid. J. Nonlinear Sci. 15(4), 255–289 (2005). https://doi.org/10.1007/s00332-004-0650-9
Zhang, A., Ma, S., Li, B., Wang, M., Guo, X., Wang, Y.: Adaptive controller design for underwater snake robot with unmatched uncertainties. Sci. China Inf. Sci 59(5), 052205 (2016). https://doi.org/10.1007/s11432-015-5421-8
Ranganathan, T., Singh, V., Thondiyath, A.: Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study. J. Mar. Sci. Technol 24(3), 968–983 (2019)
Chen, Z., Shatara, S., Tan, X.: Modeling of biomimetic robotic fish propelled by an ionic polymer–metal composite caudal fin. IEEE/ASME Trans. Mechatron. 15(3), 448–459 (2010). https://doi.org/10.1109/TMECH.2009.2027812
Chan, W.L., Kang, T.: Simultaneous determination of drag coefficient and added mass. IEEE J. Ocean. Eng. 36(3), 422–430 (2011). https://doi.org/10.1109/JOE.2011.2151370
Raj, A., Kumar, A., Thakur, A.: Automated locomotion parameter tuning for an anguilliform-inspired robot. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 002564–002569. https://doi.org/10.1109/SMC.2016.7844625 (2016)
Phamduy, P., Vazquez, M.A., Kim, C., Mwaffo, V., Rizzo, A., Porfiri, M.: Design and characterization of a miniature free-swimming robotic fish based on multi-material 3D printing. Int. J. Intell. Robot. Appl. 1(2), 209–223 (2017). https://doi.org/10.1007/s41315-017-0012-z
Nakayama, Y.: Introduction to fluid mechanics. Butterworth-Heinemann, Oxford (2018)
Tran, M.Q., Nguyen, H.D., Binns, J., Chai, S., Forrest, A.L., et al.: Least squares optimisation algorithm based system identification of an autonomous underwater vehicle. PROCEEDING of Publishing House for Science and Technology 1(1), 1–12 (2016)
Allotta, B., Costanzi, R., Pugi, L., Ridolfi, A.: Identification of the main hydrodynamic parameters of typhoon auv from a reduced experimental dataset. Ocean Eng. 147, 77–88 (2018)
Avila, J.P.J., Adamowski, J.C., Maruyama, N., Takase, F.K., Saito, M: Modeling and identification of an open-frame underwater vehicle: The yaw motion dynamics. J. Intell. Robot. Syst. 66(1-2), 37–56 (2012)
Avila, J.P., Donha, D.C., Adamowski, J.C.: Experimental model identification of open-frame underwater vehicles. Ocean Eng. 60, 81–94 (2013). https://doi.org/10.1016/j.oceaneng.2012.10.007
Randeni, P.S.A.T., Forrest, A.L., Cossu, R., Leong, Z.Q., Ranmuthugala, D., Schmidt, V.: Parameter identification of a nonlinear model: replicating the motion response of an autonomous underwater vehicle for dynamic environments. Nonlinear Dyn. 91(2), 1229–1247 (2018). https://doi.org/10.1007/s11071-017-3941-z
Xu, H., Soares, C.G.: Vector field path following for surface marine vessel and parameter identification based on LS-SVM. Ocean Eng. 113, 151–161 (2016). https://doi.org/10.1016/j.oceaneng.2015.12.037, http://www.sciencedirect.com/science/article/pii/S0029801815007040
Xu, F., Zou, Z.J., Yin, J.C., Cao, J.: Identification modeling of underwater vehicles’ nonlinear dynamics based on support vector machines. Ocean Eng. 67, 68–76 (2013). https://doi.org/10.1016/j.oceaneng.2013.02.006, http://www.sciencedirect.com/science/article/pii/S0029801813000863
Zare, E.M., Bozorg, M., Ebrahimi, S.: Identification of an autonomous underwater vehicle dynamic using extended kalman filter with ARMA noise model. Int. J. Robot. 4(6), 22–28 (2015)
Eng, Y.H., Teo, K.M., Chitre, M., Ng, K.M.: Online system identification of an autonomous underwater vehicle via in-field experiments. IEEE J. Ocean. Eng. 41(1), 5–17 (2016). https://doi.org/10.1109/JOE.2015.2403576
SAT, R.P., Leong, Z.L., Ranmuthugala, D., Forrest, A., Duffy, J.: Numerical investigation of the hydrodynamic interaction between two underwater bodies in relative motion. Appl. Ocean Res. 51, 14 – 24 (2015). https://doi.org/10.1016/j.apor.2015.02.006, http://www.sciencedirect.com/science/article/pii/S0141118715000279
Dash, A.K., Nagarajan, V., Sha, O.P.: Bifurcation analysis of a high-speed twin-propeller twin-rudder ship maneuvering model in roll-coupling motion. Nonlinear Dyn. 83(4), 2035–2053 (2016). https://doi.org/10.1007/s11071-015-2463-9
Liang, X., Li, Y., Peng, Z., Zhang, J.: Nonlinear dynamics modeling and performance prediction for underactuated AUV with fins. Nonlinear Dyn. 84(1), 237–249 (2016). https://doi.org/10.1007/s11071-015-2442-1
Kelasidi, E., Pettersen, K.Y., Gravdahl, J.T., Liljebäck, P.: Modeling of underwater snake robots. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp 4540–4547. https://doi.org/10.1109/ICRA.2014.6907522 (2014)
Mehta, R.S., Ward, A.B., Alfaro, M.E., Wainwright, P.C.: Elongation of the body in eels. Integr. Comp. Biol. 50(6), 1091–1105 (2010)
Kihiu, J., Ikua, B., Muvengei, O.: Dynamic analysis of multi-body mechanical systems with imperfect kinematic joints: A literature survey and review (2017)
Chaudhary, H., Saha, S.K.: Dynamics and balancing of multibody systems, vol. 37. Springer Science & Business Media (2008)
Kelasidi, E., Pettersen, K.Y., Liljebäck, P., Gravdahl, J.T.: Integral line-of-sight for path following of underwater snake robots. In: 2014 IEEE Conference on Control Applications (CCA), pp. 1078–1085. https://doi.org/10.1109/CCA.2014.6981478 (2014)
Fossen, T.I.: Handbook of marine craft hydrodynamics and motion control. Wiley, New York (2011)
Mobayen, S., Baleanu, D., Tchier, F.: Second-order fast terminal sliding mode control design based on LMI for a class of non-linear uncertain systems and its application to chaotic systems. J. Vib. Control. 23(18), 2912–2925 (2017). https://doi.org/10.1177/1077546315623887
Petráš, I.: A note on fractional-order non-linear controller: possible neural network approach to design. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 603–608. https://doi.org/10.1109/IJCNN.2016.7727255 (2016)
Pan, Y., Kumar, K., Liu, G.: Extremum seeking control with second-order sliding mode. SIAM J. Control. Optim. 50(6), 3292–3309 (2012). https://doi.org/10.1137/090778481
Švec, P, Thakur, A., Raboin, E., Shah, B., Gupta, S.: Target following with motion prediction for unmanned surface vehicle operating in cluttered environments. Auton. Robot. 36(4), 383–405 (2014). https://doi.org/10.1007/s10514-013-9370-z
Cardoso, V., Oliveira, J., Teixeira, T., Badue, C., Mutz, F., Oliveira-Santos, T., Veronese, L., Souza, A.F.D.: A model-predictive motion planner for the iara autonomous car. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 225–230. https://doi.org/10.1109/ICRA.2017.7989028 (2017)
Acknowledgments
This research has been supported by the Department of Science and Technology (DST), Government of India [grant number SB/FTP/ETA-44/2013]. Opinions expressed are those of the authors and do not necessarily reflect the opinions of the sponsors.
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Appendices
Appendix A: Robot Trajectories Obtained via Experiments and Simulations for Straight and Turning Motion
Appendix B: Index to Supplementary Materials
Supplementary materials | File type | Description |
---|---|---|
Multimedia Extension #1 | Video | Comparison of the trajectories obtained from the experiment and the simulation for Path: 1 |
Multimedia Extension #2 | Video | Comparison of the trajectories obtained obtained from the experiment and the simulation for Path: 2 |
Supplementary Data 1 | Estimation of low-level controller gains (kp and kd) | |
Supplementary Data 2 | .xlsx | Fitness versus generation for straight motion |
Supplementary Data 3 | .xlsx | Fitness versus generation for turning motion |
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Raj, A., Thakur, A. Hydrodynamic Parameter Estimation for an Anguilliform-inspired Robot. J Intell Robot Syst 99, 837–857 (2020). https://doi.org/10.1007/s10846-020-01154-8
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DOI: https://doi.org/10.1007/s10846-020-01154-8