Ship Route Optimization Using Hybrid Physics-Guided Machine Learning

This paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. The first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control.publishedVersio

Analyzing the Feasibility of an Unmanned Cargo Ship for Different Operational Phases

The maritime industry has begun to look into autonomous ships as an alternative to conventional ships due to growing pressure to reduce the environmental impact of maritime transportation, to increase safety, to mitigate the growing challenges in recruiting seafarers, and to increase profit margins. There is a lot of research on the challenges and feasibilities of an autonomous ship. However, there is less discussion on the transition from manned to unmanned ships and the tasks that are feasible to automate before the whole ship is unmanned. This paper investigates the technical and regulatory feasibility of automating different tasks for different operational phases for a large cargo ship. This study shows that a fully unmanned cargo ship is not feasible today, but that some tasks can be automated within the next five years.publishedVersio

DP Control System for Blueye Pioneer

I denne masteroppgaven er et feil-tolerant dynamisk posisjonering (DP) kontrollsystem utviklet for undervannsdronen Blueye Pioner. For å kunne utvikle kontrollsystemet er relevant informasjon og egenskaper relatert til dronen og dens indre sensorer i tillegg til det eksterne akustiske posisjonseringssystemet presentert. Et extended Kalman filter blir valgt som estimator og utviklet basert på en lavhastighetsmodell av dronen. Modellparametrene er estimert ved å bruke de geometriske egenskapene til dronen, Eidsviks metode for hydrodynamiske parametre, og derivative-fri optimalisering på data samlet i Marin Cybernetic's laben for å finne dempning. Relevante feilmodier for sensorene er identifisert og noen er implementert i estimatoren. For de resterende feilmodiene som ikke er implementert, er metoder for å detektere og håndtere disse foreslått. Et referansesystem basert på metoden pure pursuit er implementert for å generere referansehastigheter til kontrollsystemet. Til slutt utvikles kontrollere for jag, svai, hiv og gir. Identifikasjonen av dempingen i modellen er utfordrende og gir ikke en model med høy nøyaktighet. For å kompansere for en unøyaktig modell, blir kovariansen av prosessstøyen i estimatoren satt til en høy verdi, slik at estimatoren stoler mer på målingene enn modellen. Dette fører til at estimatene med tilhørende målinger følger målingene tett. Hastighet- og bias-estimatetene er fluktuerende. Kontrollsystemet leder dronen til ønsket posisjon i x, y, og z med en nøyaktighet på under 1m. Heading-vinkelen oscillerer rundt ønsket vinkel uten å konvergere

Ship Route Optimization Using Hybrid Physics-Guided Machine Learning

This paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. The first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control

The Importance of Documenting Autonomous Tests

This paper presents how autonomous tests can be documented and why this is important. A test area in Norway, more specifically the Trondheimsfjorden Test Area for Autonomous Ships, is used as a pilot to conduct tests with autonomous vessels and for demonstrating the procedure of documenting results. There are typically three stages in such a documentation process; 1) To register and inform about a planned test on the fjord, 2) To inform about ongoing tests and to document test results by collecting data from the vessel and from the sensor infrastructure, 3) To show historical tests and be able to do analytics or conduct learning from previous tests. The Trondheimsfjorden Test Area has been instrumented with communication and navigation infrastructure, a control centre for control and monitor of the install infrastructure and for remote operation of a ship, and a data centre for planning autonomous tests, storing data, and for sharing of test results. By documenting test results in a standardized format, this can be used to verify new technology and solutions, share knowledge and experiences, and for documentation procedures and guidelines used for the purpose. A demonstration held in The Trondheimsfjorden Test Area showed the importance of streamlining the process of conducting autonomous test and documenting them in a standardized format. This work is based on the results from the research project NAVISP-EL3-005 "Trondheimsfjorden Test Area for Autonomous Ships". The Navigation Innovation and Support Programme (NAVISP) is the programme of the European Space Agency to support the competitiveness of the European industry in the wide field of positioning, navigation and timing while supporting member states in enhancing national objectives and capabilities in the sector.publishedVersio

The Importance of Documenting Autonomous Tests

This paper presents how autonomous tests can be documented and why this is important. A test area in Norway, more specifically the Trondheimsfjorden Test Area for Autonomous Ships, is used as a pilot to conduct tests with autonomous vessels and for demonstrating the procedure of documenting results. There are typically three stages in such a documentation process; 1) To register and inform about a planned test on the fjord, 2) To inform about ongoing tests and to document test results by collecting data from the vessel and from the sensor infrastructure, 3) To show historical tests and be able to do analytics or conduct learning from previous tests. The Trondheimsfjorden Test Area has been instrumented with communication and navigation infrastructure, a control centre for control and monitor of the install infrastructure and for remote operation of a ship, and a data centre for planning autonomous tests, storing data, and for sharing of test results. By documenting test results in a standardized format, this can be used to verify new technology and solutions, share knowledge and experiences, and for documentation procedures and guidelines used for the purpose. A demonstration held in The Trondheimsfjorden Test Area showed the importance of streamlining the process of conducting autonomous test and documenting them in a standardized format. This work is based on the results from the research project NAVISP-EL3-005 "Trondheimsfjorden Test Area for Autonomous Ships". The Navigation Innovation and Support Programme (NAVISP) is the programme of the European Space Agency to support the competitiveness of the European industry in the wide field of positioning, navigation and timing while supporting member states in enhancing national objectives and capabilities in the sector