Skip to main content

A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories

  • Conference paper
  • First Online:
Innovations in Bio-Inspired Computing and Applications (IBICA 2022)


Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging.

A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others


  1. Alexandre, S., et al.: Lauv: The man-portable autonomous underwater vehicle. In: IFAC Proceedings (2012)

    Google Scholar 

  2. Aubard, M., Madureira, A., Madureira, L., Pinto, J.: Real-time automatic wall detection and localization based on side scan sonar images. In: IEEE (2022)

    Google Scholar 

  3. Calado, P., et al.: Obstacle avoidance using echo sounder sonar. In: OCEANS 2011 IEEE-Spain, pp. 1–6. IEEE (2011)

    Google Scholar 

  4. Healey, A.J.: Obstacle avoidance while bottom following for the Remus autonomous underwater vehicle. IFAC Proceedings Volumes 37(8), 251–256 (2004)

    Article  Google Scholar 

  5. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  6. Jordan, M.I.: Serial order: a parallel distributed processing approach. In: Advances in psychology, vol. 121, pp. 471–495. Elsevier (1997)

    Google Scholar 

  7. Madureira, L., et al.: The light autonomous underwater vehicle: evolutions and networking. In: 2013 MTS/IEEE OCEANS-Bergen. pp. 1–6. IEEE (2013)

    Google Scholar 

  8. Nayak, N., Nara, M., Gambin, T., Wood, Z., Clark, C.M.: Machine learning techniques for AUV side-scan sonar data feature extraction as applied to intelligent search for underwater archaeological sites. In: Field and Service Robotics (2021)

    Google Scholar 

  9. Neves, G., Ruiz, M., Fontinele, J., Oliveira, L.: Rotated object detection with forward-looking sonar in underwater applications. Expert Syst. Appl. 140, 112870 (2020)

    Article  Google Scholar 

  10. Pinto, J., Dias, P.S., Martins, R., Fortuna, J., Marques, E., Sousa, J.: The LSTS toolchain for networked vehicle systems. In: 2013 MTS/IEEE OCEANS-Bergen, pp. 1–9. IEEE (2013)

    Google Scholar 

  11. Saksvik, I.B., Alcocer, A., Hassani, V.: A deep learning approach to dead-reckoning navigation for autonomous underwater vehicles with limited sensor payloads. In: OCEANS 2021: San Diego–Porto. pp. 1–9. IEEE (2021)

    Google Scholar 

  12. Samaras, S., et al.: Deep learning on multi sensor data for counter UAV applications-a systematic review. Sensors 19(22), 4837 (2019)

    Article  Google Scholar 

  13. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, p. 132306 (2020)

    Google Scholar 

  14. Topini, E., et al.: LSTM-based dead reckoning navigation for autonomous underwater vehicles. In: Global Oceans 2020: Singapore–US Gulf Coast. pp. 1–7. IEEE (2020)

    Google Scholar 

  15. Yan, Z., Li, J., Jiang, A., Wang, L.: An obstacle avoidance algorithm for AUV based on obstacle’s detected outline. In: 2018 37th Chinese Control Conference (CCC), pp. 5257–5262. IEEE (2018)

    Google Scholar 

  16. Zhang, X., He, B., Li, G., Mu, X., Zhou, Y., Mang, T.: Navnet: AUV navigation through deep sequential learning. IEEE Access 8, 59845–59861 (2020)

    Article  Google Scholar 

Download references


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956200. For more info, please visit

Author information

Authors and Affiliations


Corresponding author

Correspondence to László Antal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Antal, L. et al. (2023). A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham.

Download citation

Publish with us

Policies and ethics