Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios

Nadav Cohen, Zeev Yampolsky, Itzik Klein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Underwater Technology, UT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350331752
DOIs
StatePublished - 2023
Event2023 IEEE International Symposium on Underwater Technology, UT 2023 - Tokyo, Japan
Duration: 6 Mar 20239 Mar 2023

Publication series

Name2023 IEEE International Symposium on Underwater Technology, UT 2023

Conference

Conference2023 IEEE International Symposium on Underwater Technology, UT 2023
Country/TerritoryJapan
CityTokyo
Period6/03/239/03/23

Bibliographical note

Funding Information:
N.C. is supported by the Maurice Hatter Foundation and University of Haifa presidential scholarship for students on a direct Ph.D. track.

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Autonomous underwater vehicle (AUV)
  • Deep Learning
  • Doppler velocity log (DVL)
  • Inertial navigation system (INS)
  • Transformer

ASJC Scopus subject areas

  • Oceanography
  • Automotive Engineering
  • Acoustics and Ultrasonics
  • Instrumentation

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