Smart Technologies for Unmanned Surface Vessels: On the Path Towards Full Automation

Research output: ThesisLicentiate Thesis

Abstract

As for the automotive industry, large efforts are being made by industry and
academia to create autonomous ships. Te solutions for this is very technologyintense, as many building blocks, often relying on AI technology, need to work
together to create a complete system that is safe and reliable to use. Even when
the ships are fully unmanned, humans are still foreseen to guide the ships when
unknown situations arise. Tis will be done through teleoperation systems.
In this thesis, methods are presented to enhance the capability of two building blocks that are important for autonomous ships; a positioning system, and a
system for remote supervision.
Te positioning system has been constructed to not rely on GPS (Global Positioning System), as this system can be jammed or be spoofed. Instead, it uses
Bayesian calculations to compare the bottom depth and magnetic field measurements with known sea charts and magnetic field maps, in order to estimate the
position. State-of-the-art techniques for this method normally use low-accuracy
navigation sensors and high-resolution maps. Te problem is that there are hardly
any high-resolution maps available in the world, hence we present a method of the
opposite; namely using high-accuracy navigation sensors and low-resolution maps
(normal sea charts). Te results from a 20h test-run gave a mean position error of
10.2m, which would in most cases be accurate enough for navigation purpose.
In the second building block, we investigated, how 3D and VR approaches
could support the remote operation of unmanned ships with a low bandwidth
connection, by comparing respective GUIs with a Baseline GUI following the currently applied interfaces in such contexts. Our findings show, that both the 3D
and VR approaches outperform the traditional approach significantly. We found
the 3D GUI and VR GUI users to be better at reacting to potentially dangerous
situations compared to the Baseline GUI users, and they could keep track of the
surroundings more accurately.

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Subject classification (UKÄ) – MANDATORY

  • Computer Vision and Robotics (Autonomous Systems)

Keywords

Translated title of the contributionSmart teknologi för obemannade ytfartyg: På vägen mot full automation
Original languageEnglish
QualificationLicentiate
Awarding Institution
Supervisors/Assistant supervisor
Award date2019 Feb 4
Place of PublicationLund
Edition1
Print ISBNs978-91-7753-960-5
Electronic ISBNs978-91-7753-961-2
Publication statusPublished - 2019 Feb 4
Publication categoryResearch

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