Results

Publications

SimuShips -- A High Resolution Simulation Dataset for Ship Detection with Precise Annotations. Raza, M., Prokopova, H., Huseynzade, S., Azimi, S. & Lafond, S. Computer Vision and Pattern Recognition: arXiv:2211.05237. Cornell University. 22 September 2022. https://doi.org/10.48550/arXiv.2211.05237
Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development. Raza M., Prokopova, H., Huseynzade, S., Azimi, S., Lafond, S. Journal of Marine Science and Engineering. 2022; 10(10):1469. https://doi.org/10.3390/jmse10101469
Torque estimation in marine propulsion systems. Manngård, M., Koene, I., Lund, W., Haikonen, S., Fagerholm, F.A., Wilczek, M., Mnich, K., Keski-Rahkonen J., Viitala, R., Björkqvist, J. & Toivonen, H.T. Mechanical Systems and Signal Processing, Volume 172, 1 June 2022, 108969. https://www.sciencedirect.com/science/article/pii/S0888327022001480
An Experimental Research Platform for Maritime Automation and Autonomous Surface Ship Applications. Brushane, K. Jämsä, S. Lafond & J. Lilius.,2021. 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles. IFAC CAMS September 22 – 24, 2021. https://research.abo.fi/en/publications/a-experimental-research-platform-for-maritime-automation-and-auto
Aboa Mare Remote Operation Center (AMROC). J. Salokannel & M. Salokorpi, 2021, Proceedings of the 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, 7-11 June 2021, Glasgow, Scotland, UK
Formal Development of Multi-vessel Navigation of Maritime Autonomous Systems using UPPAAL STRATEGO, Shokri-Manninen, F., Vain, J., Waldén, M.,2021. Proceedings of the 32nd Nordic Workshop on Programming Theory. Reykjavik University, Reykjavik, Iceland, November 4 – 6, 2021. https://research.abo.fi/en/publications/formal-development-of-multi-vessel-navigation-of-maritime-autonom
An Experimental Research Platform for Maritime Automation and Autonomous Surface Ship Applications, F. Brushane, K. Jämsä, S. Lafond & J. Lilius. (2021). 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles. IFAC CAMS September 22–24, 2021. https://research.abo.fi/en/publications/a-experimental-research-platform-for-maritime-automation-and-auto
Challenges of Artificial Intelligence and Machine Learning Software in Autonomous Vessels. Ashraf, A., Lilius, J., Porres Paltor, I., Walden, M. & Petre, L., 2021, Proceedings of International Seminar on Safety and Security of Autonomous Vessels (ISSAV’19). p. https://research.abo.fi/en/publications/challenges-of-artificial-intelligence-and-machine-learning-softwa
Prediction of on-board energy usage combining physics-based modeling and machine learning. Björkqvist, J., Manngård, M., Gustafsson, W., Böling, J. & Hammarström, J., May 2021, 3rd International Conference on Modelling and Optimisation of Ship Energy Systems. Espoo. https://research.abo.fi/en/publications/prediction-of-on-board-energy-usage-combining-physics-based-model
ABOships – An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations. Iancu, B., Soloviev, V., Zelioli, L. & Lilius, J., 5 Feb 2021, In: Remote Sensing. 13, 5, p. 1-17 17 p., 988. https://research.abo.fi/en/publications/aboships-an-inshore-and-offshore-maritime-vessel-detection-datase
A Systematic Mapping Study on Edge Computing Approaches for Maritime Applications. Morariu, A-R., Ashraf, A. & Björkqvist, J., 1 Sep 2021, Euromicro DSD/SEAA 2021. IEEE Computer Society Conference Publishing Services (CPS), 8 p. https://research.abo.fi/en/publications/a-systematic-mapping-study-on-edge-computing-approaches-for-marit
Formal Verification of COLREG-Based Navigation of Maritime Autonomous Systems. Shokri-Manninen, F., Vain, J., Walden, M., de Boer, F. (Ed.), & Cerone, A. (Ed.) (2020). In Proceedings of SEFM 2020 -The 18th International Conference on Software Engineering and Formal Methods (pp. –) https://research.abo.fi/en/publications/formal-verification-of-colreg-based-navigation-of-maritime-autono
A survey of machine learning approaches for surface maritime navigation. Azimi, S., Salokannel, J., Lafond, S., Lilius, J., Salokorpi, M. & Porres, I., 2020, Maritime Transport VIII: proceedings of the 8th International Conference on Maritime Transport: Technology, Innovation and Research: Maritime Transport ’20. Iniciativa Digital Politècnica, p. 103 117 p. https://research.abo.fi/en/publications/a-survey-of-machine-learning-approaches-for-surface-maritime-navi
On the Verification and Validation of AI Navigation Algorithms.Porres, I., Azimi, S., Lafond, S., Lilius, J., Salokannel, J. & Salokorpi, M., 2020, Global OCEANS 2020. https://research.abo.fi/en/publications/on-the-verification-and-validation-of-ai-navigation-algorithms
Scenario-based Testing of a Ship Collision Avoidance System. Porres, I., Azimi, S. & Lilius, J., 2020, 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, p. 545-552.https://research.abo.fi/en/publications/scenario-based-testing-of-a-ship-collision-avoidance-system
Edge-based Vibration Monitoring of Marine Vessel Engines. Morariu, A-R., Lund, W., Lundell, A., Björkqvist, J. & Anders, Ö., 14 Oct 2020, 12th Symposium on High-Performance Marine Vehicles: HIPER’20. Volker, B. (ed.). Technische Universität Hamburg-Harburg, p. 239-250 12 p. https://research.abo.fi/en/publications/edge-based-vibration-monitoring-of-marine-vessel-engines
Using Digital Twin Technology to Ensure Data Quality in Transport Systems. Björkqvist, J., Manngård, M. & Lund, W., 2020, Proceedings of TRA2020, the 8th Transport Research Arena: Rethinking transport – towards clean and inclusive mobility. Helsinki, (Traficom Research Reports; no. 7). https://research.abo.fi/en/publications/using-digital-twin-technology-to-ensure-data-quality-in-transport
Comparing CNN-Based Object Detectors on Two Novel Maritime Datasets. Soloviev, V., Farahnakian, F., Zelioli, L., Iancu, B., Lilius, J. & Heikkonen, J., 2020, 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE https://research.abo.fi/en/publications/comparing-cnn-based-object-detectors-on-two-novel-maritime-datase
Estimation of propeller torque in azimuth thrusters. Manngård, M., Lund, W., Keski-Rahkonen, J., Nänimäinen, J., Saarela, V-P., Björkqvist, J. & Toivonen, H., 2019, In: IFAC-PapersOnLine. 52, 21, p. 140–145 https://research.abo.fi/en/publications/estimation-of-propeller-torque-in-azimuth-thrusters
Virtual sensing in marine systems. Manngård, M., Lund, W., Björkqvist, J., Toivonen, H., Sin, G. (ed.), Bagterp Jørgensen, J. (ed.) & Kjøbsted Huusom, J. (ed.), 2019. https://research.abo.fi/en/publications/virtual-sensing-in-marine-systems
Building and operating a UHF-band test network for providing mission critical marine communication in the Turku archipelago. Björkqvist, J., Lund, W., Soloviev, V., Tuulos, K. & Suominen, K., 2018, Åbo Akademi University. 17 p. https://research.abo.fi/en/publications/building-and-operating-a-uhf-band-test-network-for-providing-miss
IoT at Sea. Nybom, K., Lund, W., Lafond, S., Lilius, J., Björkqvist, J., Suominen, K. & Tuulos, K., 2018, 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, p. 1–7 7 p. https://research.abo.fi/en/publications/iot-at-sea

Theses

Deep learning approaches for maritime ship detection from inshore and offshore imagery. Winsten, Jesper (2022).
https://www.doria.fi/handle/10024/185309
Application of a Distributed Software System on an Autonomous Maritime Vehicle. Brushane, Fredrik (2021).
https://www.doria.fi/handle/10024/181262
Development of autonomous navigation systems for maritime applications. Storbacka, Mathias (2021).
https://www.doria.fi/handle/10024/181815
Learning autonomous maritime navigation with offline reinforcement learning and marine traffic data. Westerlund, Jimmy (2021).
https://www.doria.fi/handle/10024/181408
Reglersystem för kurs och hastighet för autonomt fartyg. Fröjdö, Linus (2021). Bachelor’s thesis; not available in public.
Learning Maritime Surface Ship by Imitation Learning. Landais, Clément (2021).
https://www.doria.fi/handle/10024/181525
Identifying Risk-Prone Behavior of Seafarers by Using Explainable AI. Fouqué, Nicolas (2021).
https://www.doria.fi/handle/10024/181583
Integration of ML models in the 3D simulation environment AISimLive. Aarnio, Juha (2021).
https://www.doria.fi/handle/10024/182189
COLREG compliant collision avoidance using reinforcement learning. Penttinen, Sebastian (2020).
https://www.doria.fi/handle/10024/177467
A simulator for evaluating machine-learning algorithms for autonomous ships. Hupponen, Kim (2020).
https://www.doria.fi/handle/10024/177441
Learning Maritime Surface Ship Navigation by Reinforcement Learning: with a focus on designing the framework and collecting the input set. Nyberg, Richard. (ongoing)
Learning Maritime Surface Ship Navigation by Reinforcement Learning: with a focus on the building the model. Cucic, Tatiana. (ongoing)
Fuzzy logic and unmanned surface vehicles: Implementing collision avoidance in Python. Aura, Emil (2018).
https://www.doria.fi/handle/10024/163340
Object Detection Using LIDAR In Maritime Scenarios. Wessman, Michael. (2018).
https://www.doria.fi/handle/10024/156601

Results

Publications

Theses

Reports