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Autonomous navigation for a depth measuring vehicle in an unknown naval environment
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The accuracy of depth measurements in shallow parts of the Stockholm archipelago nautical charts is relatively low at present. In order to improve it, an autonomous depth measuring craft equipped with three downward facing sonar sensors is being developed at KTH.

In this report, a navigation algorithm that does not require any prior knowledge of the target environment is proposed to be implemented in the craft. It consists of a predifined sweeping pattern, a contour following that only relies on sensor data, and a way of identifying and scanning missed partitions. Performance and the impact of sensor inaccuracies in addition to environmental factors, such as wind and waves, are investigated through simulations.

Results from simulations show that the proposed navigation algorithm is able to complete scans of complex environments. However, the uncertainty in sensor readings is proven to have a large inuence on performance.

Finally, further improvements to the algorithm are proposed and the realism of the simulation is discussed.

Abstract [sv]

Noggrannheten av djupmätningar i grunda delar av Stockholms skärgårds sjökort är för tillfället bristfälliga. För att förbättra dessa utvecklar KTH en autonom djupmätningsfarkost, utrustad med tre nedåtriktade sonarer. I denna rapport föreslås en navigeringsalgoritm för denna farkost. Algoritmen behöver inte någon tidigare kännedom om området och består av ett fördefinierat svepande sökmönster, en metod som endast använder sonardata för att följa land och ett sätt att identifiera samt täcka ej besökta områden. Prestanda och påverkan av störningar i form av sensorosäkerheter och miljöfaktorer, som vind och vågor, undersöks genom simuleringar. Resultat från simuleringarna visar att den föreslagna algoritmen klarar av att täcka komplexa områden. Däremot har störningar i sensordata stor påverkan på prestandan. Till slut föreslås förbättringar och simuleringens realism diskuteras.

Place, publisher, year, edition, pages
2015. , 32 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-168311OAI: oai:DiVA.org:kth-168311DiVA: diva2:815687
Supervisors
Available from: 2015-06-01 Created: 2015-06-01 Last updated: 2015-06-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Output format
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