Skip to main content

Path Planning for Underwater Gliders with Motion Constraints

  • Conference paper
  • First Online:
Mechanism and Machine Science (ASIAN MMS 2016, CCMMS 2016)

Abstract

The underwater glider technology is a promising ocean observing technique. This paper presents path planning for underwater gliders as they travel in the water. The objective is that the underwater glider arrives at the destined depth while avoiding the obstacles in the way. Artificial potential field approach is used in the path planning algorithm, which is featured by adding motion constraints of the underwater glider into path generation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Merckelbach L (2013) On the probability of underwater glider loss due to collision with a ship. J Mar Sci Technol 18:75–86

    Article  Google Scholar 

  2. Witt J, Dunbabin M (2008) Go with the flow: optimal AUV path planning in coastal environments. In: 2008 Australian conference on robotics and automation, 2008

    Google Scholar 

  3. Zhang M, Inanc T, Ober-Blobaum S, Marsden JE (2008) Optimal trajectory generation for a glider in time-varying 2D ocean flows B-spline model. In: 2008 IEEE international conference on robotics and automation. IEEE Press, New Jersey, pp. 1083–1088

    Google Scholar 

  4. Rao D, Williams SB (2009) Large-scale path planning for underwater gliders in ocean currents. In: 2009 Australasian conference on robotics and automation

    Google Scholar 

  5. Thompson DR, Chien S, Chao Y, Li P, Cahill B, Levin J, Schofield O, Balasuriya A, Petillo S, Arrott M, Meisinger M (2010) Spatiotemporal path planning in strong, dynamic, uncertain currents. In: 2010 IEEE international conference on robotics and automation. IEEE Press, New Jersey, pp. 4778–4783

    Google Scholar 

  6. Isern-Gonzalez J, Hernandez-Sosa D, Fernandez-Perdomo E, Cabrera-Gamez J, Dominguez-Brito AC, Prieto-Maranon V (2011) Path planning for underwater gliders using iterative optimization. In: 2011 IEEE international conference on robotics and automation. IEEE Press, New Jersey, pp. 1538–1543

    Google Scholar 

  7. Lolla T, Lermusiaux PFJ, Uechermann MP, Haley PJ Jr (2014) Time-optimal path planning in dynamic flows using level set equations: theory and schemes. Ocean Dyn 64:1373–1397

    Article  Google Scholar 

  8. Eichhorn M (2015) Optimal routing strategies for autonomous underwater vehicles in time-varying environment. Robot Auton Syst 67:33–43

    Article  Google Scholar 

  9. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5:90–99

    Article  Google Scholar 

  10. Chanclou B, Luciani A (1996) Global and local path planning in natural environment by physical modeling. In: Intelligent robots and systems ’96, pp. 1118–1125

    Google Scholar 

  11. Plumer E (1992) Neural network structure for navigation using potential fields. In: International joint conference on neural networks’92, pp. 327–332

    Google Scholar 

  12. Akishita S, Hisanobu T, Kawamura S (1993) Fast path planning available for moving obstacle avoidance by use of laplace potential. In: 1993 IEEE intelligent robots and systems, pp. 673–678

    Google Scholar 

  13. Makita Y, Hagiwara M, Nakagawa M (1994) A simple path planning system using fuzzy rules and a potential field. In: 1994 IEEE world congress on computational intelligence, pp. 994–999

    Google Scholar 

  14. Wu K, Chen C, Lee J (1996) Genetic-based adaptive fuzzy controller for robot path planning. In: 1996 IEEE international conference on fuzzy systems, pp. 1687–1692

    Google Scholar 

  15. Wang Y, Zhang H, Wang S (2009) Trajectory control strategies for the underwater glider. In: 2009 IEEE international conference on measuring technology and mechatronics automation. IEEE Press, New Jersey, pp. 918–921

    Google Scholar 

  16. Jenkins SA, Humphreys DE, Sherman J, Osse J, Jones C, Leonard N, Graver J, Bachmayer R, Clem T, Carroll P, Davis P, Berry J, Worley P, Wasyl J (2003) Underwater glider system study. Technical report, Scripps Institution of Oceanography

    Google Scholar 

  17. Yang Y, Wang S, Wu Z (2010) Multi-AUV coordination in the underwater environment with obstacles. In: IEEE Oceans 2010. IEEE Press, New Jersey, pp. 1–6

    Google Scholar 

  18. Yang Y, Wang S, Wu Z, Wang Y (2011) Motion planning for multi-HUG formation in an environment with obstacles. Ocean Eng 38:2262–2269

    Article  Google Scholar 

  19. Wang S, Liu F, Shao S, Wang Y, Niu W, Wu Z (2014) Dynamic modeling of hybrid underwater glider based on the theory of differential geometry and sea trials. J Mech Eng 50:19–27 (in Chinese)

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the reviewers for their constructive comments. We also gratefully acknowledge the support from the National Natural Science Foundation of China (Grant No. 51205277, 51475319) and State Oceanic Administration of China (Grant No. cxsf2014-33).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhiliang Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Wu, Z. et al. (2017). Path Planning for Underwater Gliders with Motion Constraints. In: Zhang, X., Wang, N., Huang, Y. (eds) Mechanism and Machine Science . ASIAN MMS CCMMS 2016 2016. Lecture Notes in Electrical Engineering, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-2875-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2875-5_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2874-8

  • Online ISBN: 978-981-10-2875-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics