Sea Docking by Dual-eye Pose Estimation with Optimized Genetic Algorithm Parameters

Abstract

Three-dimensional (3D) estimation of position and orientation (pose) using dynamic (successive) images input at video rates needs to be performed rapidly when the estimated pose is used for real-time feedback control. Single-camera 3D pose estimation has been studied thoroughly, but the estimated position accuracy in the camera depth of field has proven insufficient. Thus, docking systems for underwater vehicles with single-eye cameras have not reached practical application. The authors have proposed a new 3D pose estimation method with dual cameras that exploits the parallactic nature of stereoscopic vision to enable reliable 3D pose estimation in real time. We call this method the “real-time multi-step genetic algorithm (RM-GA).” However, optimization of the pose tracking performance has been left unchallenged despite the fact that improved tracking performance in the time domain would help improve performance and stability of the closed-loop feedback system, such as visual servoing of an underwater vehicle. This study focused on improving the dynamic performance of dual-eye real-time pose tracking by tuning RM-GA parameters and confirming optimization of the dynamical performance to estimate a target marker’s pose in real time. Then, the effectiveness and practicality of the real-time 3D pose estimation system was confirmed by conducting a sea docking experiment using the optimum RM-GA parameters in an actual marine environment with turbidity.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Aoyagi, S., Hattori, N., Kohama, A., Komai, S., Suzuki, M., Takano, M., Fukui, E.: Object detection and recognition using template matching with SIFT features assisted by invisible floor marks. J. Robot. Mechatron. 21(6), 689 (2009)

    Article  Google Scholar 

  2. 2.

    Tomono, M.: 3D object modeling and segmentation using image edge points in cluttered environments. J. Robot. Mechatron. 21(6), 672 (2009)

    Article  Google Scholar 

  3. 3.

    Brandou, V., Allais, A.G., Perrier, M., Malis, E., Rives, P., Sarrazin, J., Sarradin, P.M.: 3D reconstruction of natural underwater scenes using the stereovision system iris. In: OCEANS 2007-Europe, pp. 1–6. IEEE (2007)

  4. 4.

    Manikandan, G., Sridevi, S., Dhanasekar, J.: Vision based autonomous underwater vehicle for pipeline tracking, International Journal of Innovative Research in Science, Engineering and Technology, vol. 1, pp. 2347–6710 (2015)

  5. 5.

    Eustice, R.M., Pizarro, O., Singh, H.: Visually augmented navigation for autonomous underwater vehicles. IEEE J. Ocean. Eng. 33(2), 103–122 (2008)

    Article  Google Scholar 

  6. 6.

    Ghosh, S., Ray, R., Vadali, S.R., Shome, S.N., Nandy, S.: Reliable pose estimation of underwater dock using single camera: a scene invariant approach. Mach. Vis. Appl. 27(2), 221–36 (2016)

    Article  Google Scholar 

  7. 7.

    Kume, A., Maki, T., Sakamaki, T., Ura, T.: A method for obtaining high-coverage 3D images of rough seafloor using AUV-real-time quality evaluation and path-planning. J. Robot. Mechatron. 25(2), 364–374 (2013)

    Article  Google Scholar 

  8. 8.

    Guo, J.: Mooring cable tracking using active vision for a biomimetic autonomous underwater vehicle. J. Mar. Sci. Technol. 13(2), 147–15 (2008)

    Article  Google Scholar 

  9. 9.

    Vallicrosa, G., Bosch, J., Palomeras, N., Ridao, P., Carreras, M., Gracias, N.: Autonomous homing and docking for AUVs using Range-Only Localization and Light Beacons. IFAC-PapersOnLine 49(23), 54–60 (2016)

    MathSciNet  Article  Google Scholar 

  10. 10.

    Palomeras Rovira, N., Peñalver, A., Campos Massot, M., Black, PL, Fernández, J.J., Ridao Rodríguez, P., Oliver Codina, G.: i-AUV Docking Panel and Intervention at Sea. Sensors 16(10), 1673 (2016)

    Article  Google Scholar 

  11. 11.

    Myint, M., Yonemori, K., Lwin, KN., Yanou, A., Minami, M.: Dual-eyes Vision-based Docking System for Autonomous Underwater Vehicle: an Approach and Experiments, J Intell Robot Syst. https://doi.org/10.1007/s10846-017-0703-6 (2017)

    Article  Google Scholar 

  12. 12.

    Uddin, M., Abido, M.A., Rahman, M.A.: Nasir Real-Time implementation of a genetic algorithm based fuzzy logic controller for interior permanent magnet synchronous motor drive proceeding of ICECE (2002)

  13. 13.

    Nguyen, V.B., Morris, A.S.: Genetic algorithm tuned fuzzy logic controller for a robot arm with two-link flexibility and two-joint elasticity. J. Intell. Robot. Syst. 49(1), 3–18 (2007)

    Article  Google Scholar 

  14. 14.

    Park, J.-Y., Jun, B.-H., Lee, P.-M., Oh, J.: Experiments on vision guided docking of an autonomous underwater vehicle using one camera. IEEE J. Ocean. Eng. 36(1), 48–61 (2009)

    Article  Google Scholar 

  15. 15.

    Palomeras, N., Ridao, P., Ribas, D., Vallicrosa, G.: Autonomous i-AUV docking for fixed-base manipulation, in Proc. Int. Fed. Autom. Control 47(3), 12160–12165 (2014)

    Google Scholar 

  16. 16.

    Rao, R.V., Savsani, V.J.: Advance Optimization Techniques. Mechanical design optimization using advanced optimization techniques, pp. 5–34. Springer Science & Business Media (2012)

  17. 17.

    Preechakul, C., Kheawhom, S.: Modified genetic algorithm with sampling techniques for chemical engineering optimization. J. Ind. Eng. Chem. 15(1), 110–118 (2009)

    Article  Google Scholar 

  18. 18.

    Cui, H., Turan, O.: Application of a new multi-agent hybrid co-evolution based particle swarm optimization methodology in ship design. Comput.-Aided Des. 2, 1013–1027 (2010)

    Article  Google Scholar 

  19. 19.

    Zou, Y., Luo, D.: A modified ant colony algorithm used for multi-robot odor source localization. In: International Conference on Intelligent Computing, pp. 502–509. Springer, Berlin (2008)

  20. 20.

    Rexhepi, A., Maxhuni, A., Dika, A.: Analysis of the impact of parameters values on the Genetic Algorithm for TSP. Int. J. Comput. Sci. Issues 10(1), 158–164 (2013)

    Google Scholar 

  21. 21.

    Boyabatli, O., Sabuncuoglu, I.: Parameter selection in genetic algorithms, Journal of Systemics. Cybern. Inf. 4(2), 78 (2004)

    Google Scholar 

  22. 22.

    Tabassum, M., Mathew, K.: A genetic algorithm analysis towards optimization solutions. Int. J. Digit. Inf. Wirel. Commun. (IJDIWC) 4(1), 124–142 (2014)

    Google Scholar 

  23. 23.

    Roeva, O., Fidanova, S., Paprzycki, M.: Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. In: 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 371–376. IEEE (2013)

  24. 24.

    Lwin, K.N., Yonemori, K., Myint, M., Mukada, N., Minami, M., Yanou, A., Matsuno, T.: Performance analyses and optimization of real-time multi-step GA for visual-servoing based underwater vehicle, Techno-Ocean 2016. IEEE (2016)

  25. 25.

    Yu, F., Minami, M., Song, W., Zhu, J., Yanou, A.: On-line head pose estimation with binocular hand-eye robot based on evolutionary model-based matching. J. Comput. Inf. Technol. 2(1), 43–54 (2012)

    Google Scholar 

  26. 26.

    Minami, M., Agbanhan, J., Asakura, T.: Evolutionary Scene Recognition and Simultaneous Position Orientation Detection. In: Soft Computing in Measurement and Information Acquisition, pp. 178–207. Springer, Berlin (2003)

    Google Scholar 

  27. 27.

    Mehrez, M.W., Mann, G.K., Gosine, R.G.: An optimization based approach for relative localization and relative tracking control in multi-robot systems. J. Intell. Robot. Syst. 85(2), 385–408 (2017)

    Article  Google Scholar 

  28. 28.

    GiriRajkumar, S.M., Ramkumar, K., Sanjay, S.O.V.: Real time application of ants colony optimization. Int. J. Comput. Appl. 3(8), 0975–8887 (2010)

    Google Scholar 

  29. 29.

    Mousavian, S.H., Koofigar, H.R.: Identification-based robust motion control of an AUV: optimized by particle swarm optimization algorithm. J. Intell. Robot. Syst. 85(2), 331–352 (2017)

    Article  Google Scholar 

  30. 30.

    Song, W., Fujia, Y., Minami, M.: 3D visual servoing by feedforward evolutionary recognition. J. Adv. Mech. Des. Syst. Manuf. 4(4), 739–755 (2010)

    Article  Google Scholar 

  31. 31.

    Suzuki, H., Minami, M.: Visual servoing to catch fish using global/local GA search. IEEE/ASME Trans. Mechatron. 10(3), 352–357 (2005)

    Article  Google Scholar 

  32. 32.

    Myint, M., Yonemori, K., Yanou, A., Minami, M., Ishiyama, S.: Visual-servo-based autonomous docking system for underwater vehicle using dual-eyes camera 3D-pose tracking. In: 2015 IEEE/SICE International Symposium on System Integration(SII), pp. 989–994 (2015)

  33. 33.

    Myint, M., Yonemori, K., Yanou, A., Lwin, K.N., Minami, M., Ishiyama, S.: Visual-based deep sea docking simulation of underwater vehicle using dual-eyes cameras with lighting adaptation. In: Proceedings of OCEAN 2016-Shanghai, pp. 1–8 (2016)

  34. 34.

    Myint, M., Yonemori, K., Yanou, A., Lwin, K.N., Mukada, N., Minami, M.: Dual-eyes visual-based sea docking for sea Bottom battery recharging. OCEAN (2016)

  35. 35.

    Lwin, K.N., Yonemori, K., Myint, M., Yanou, A., Minami, M.: Autonomous docking experiment in the sea for visual-servo type underwater vehicle using three-dimensional marker and dual-eyes cameras. In: Society of Instrument and Control Engineers of Japan (SICE), 2016 55th Annual Conference, pp. 1359–1365. IEEE (2016)

  36. 36.

    Myint, M., Yonemori, K., Yanou, A., Lwin, K.N., Minami, M., Ishiyama, S.: Visual servoing for underwater vehicle using dual-eyes evolutionary real-time pose tracking. J. Robot. Mechatron. 28(4), 543–558 (2016)

    Article  Google Scholar 

  37. 37.

    Myint, M., Yonemori, K., Yanou, A., Ishiyama, S., Minami, M.: Robustness of visual-servo against air bubble disturbance of underwater vehicle system using three-dimensional marker and dual-eye cameras. In: OCEANS 2015-MTS/IEEE, pp. 1–8, Washington (2015)

Download references

Acknowledgements

This work supported by JSPS KAKENHI Grant Number JP16K06183. The authors would like to thank Mitsui E&S Shipbuilding Co., Ltd.; and Kowa Corporation for their collaboration and support for this study. The authors would also like to express their thanks to Dr. Yuya Nishida, Prof. Kazuo Ishii, Prof. Toshihiko Maki, and Prof. Tamaki Ura for their helps and supports.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Khin Nwe Lwin.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lwin, K.N., Myint, M., Mukada, N. et al. Sea Docking by Dual-eye Pose Estimation with Optimized Genetic Algorithm Parameters. J Intell Robot Syst 96, 245–266 (2019). https://doi.org/10.1007/s10846-018-0970-x

Download citation

Keywords

  • Real-time multi-step GA
  • Visual servoing
  • Pose estimation
  • Dual-eye tracking
  • Underwater docking