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Learning Based Target Following Control for Underwater Vehicles

  • Zhou Hao
  • Huang HaiEmail author
  • Zhou Zexing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

Target following of underwater vehicles has attracted increasingly attentions on their potential applications in oceanic resources exploration and engineering development. However, underwater vehicles confront with more complicated and extensive difficulties in target following than those on the land. This study proposes a novel learning based target following control approach through the integration of type-II fuzzy system and support vector machine (SVM). The type-II fuzzy system allows researchers to model and minimize the effects of uncertainties of changing environment in the rule-based systems. In order to improve the vehicle capacity of self-learning, an SVM based learning approach has been developed. Through genetic algorithm generating and mutating fuzzy rules candidate, SVM learning and optimization, one can obtain optimized fuzzy rules. Tank experiments have been performed to verify the proposed controller.

Keywords

Underwater vehicle Machine learning Target following 

Notes

Acknowledgements

This project is supported by National Science Foundation of China (No. 61633009, 51579053, 5129050), it is also supported by the Field Fund of the 13th Five-Year Plan for the Equipment Pre-research Fund (No. 61403120301). All these supports are highly appreciated.

References

  1. 1.
    Benedetto, A., Roberto, C., Riccardo, C., Francesco, F., Jonathan, G., Enrico, M., NiccolÓ, M., Alessandro, R., Andrea, R.: A low cost autonomous underwater vehicle for patrolling and monitoring. J. Eng. Marit. Environ. 231(3), 740–749 (2017)Google Scholar
  2. 2.
    Mansour, K., Hsiu, M.W., Chih, L.H.: Nonlinear trajectory-tracking control of an autonomous underwater vehicle. Ocean Eng. 145, 188–198 (2017)CrossRefGoogle Scholar
  3. 3.
    Myo, M., Kenta, Y., Akira, Y., Mamoru, M., Shintaro, I.: 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), 11–13 December, Meijo University, Nagoya, Japan, pp. 989–994 (2015)Google Scholar
  4. 4.
    Somaiyeh, M.Z., David, M.W., Powers, K.S.: An autonomous reactive architecture for efficient AUV mission time management in realistic dynamic ocean environment. Robot. Auton. Syst. 87, 81–103 (2017)CrossRefGoogle Scholar
  5. 5.
    Taha, E., Mohamed, Z., Kamal, Y.T.: Terminal sliding mode control for the trajectory tracking of underactuated Autonomous Underwater Vehicles. Ocean Eng. 129, 613–625 (2017)CrossRefGoogle Scholar
  6. 6.
    Yanwu, Z., Brian, K., Jordan, M. S., Robert, S. McEwen, et al.: Isotherm tracking by an autonomous underwater vehicle in drift mode. IEEE J. Ocean. Eng. 42(4), 808–817 (2017)Google Scholar
  7. 7.
    Khoshnam, S., Mehdi, D.: Line-of-sight target tracking control of underactuated autonomous underwater vehicles. Ocean Eng. 133, 244–252 (2017)CrossRefGoogle Scholar
  8. 8.
    Xue, Q.: Spatial target path following control based on Nussbaum gain method for underactuated underwater vehicle. Ocean Eng. 104, 680–685 (2015)CrossRefGoogle Scholar
  9. 9.
    Enric, G., Ricard, C., Narcís, P., David, R., et al.: Coverage path planning with real-time replanning and surface reconstruction for inspection of three-dimensional underwater structures using autonomous underwater vehicles. J. Field Robot. 32(7), 952–983 (2015)CrossRefGoogle Scholar
  10. 10.
    Marc, C., Junku, Y., Joan, B., Pere, R.: A behavior-based scheme using reinforcement learning for autonomous underwater vehicles. IEEE J. Ocean. Eng. 30(2), 416–427 (2005)CrossRefGoogle Scholar
  11. 11.
    Mae, L.S.: Marine Robot Autonomy. Springer, New York (2013)Google Scholar
  12. 12.
    Jong, W.P., Hwan, J.K., Young, C.K., Dong, W.K.: Advanced fuzzy potential field method for mobile robot obstacle avoidance. Comput. Intell. Neurosci. 2016, 13 (2016). Article ID 6047906Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Key Laboratory of Science and Technology for Autonomous Underwater VehicleHarbin Engineering UniversityHarbinChina

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