Research output per year
Research output per year
Project Details
Description
The purpose of this project is to develop techniques to let a computer obtain reliable situation awareness for a vessel at sea. Based on state of art sensor technology, algorithms will interpret information about surroundings and predict behaviours of other vessels. When information confirms deviations from anticipated normal behaviours, algorithms shall predict imminent risks and provide decision support for safe manoeuvring. One aim is that results of risk assessment and risk avoidance can be presented as advice to a human navigator. Another aim is that this information can be presented to an autonomous manoeuvring system that can respond with a plan for immediate manoeuvring to avoid risk of collision or grounding. The project aims to demonstrate the technology at a proof of concept level, first against simulated scenarios at SIMAC, then using live data from one or more vessels in Danish waters.
| Acronym | ASAN |
|---|---|
| Status | Finished |
| Effective start/end date | 01/12/2018 → 31/03/2021 |
Collaborative partners
- Technical University of Denmark (lead)
- Svendborg International Maritime Academy (Project partner)
- Wartsila Lyngsø Marine A/S (Project partner)
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Keywords
- autonomous navigation
- perception in a marine environment
- anticipation in a marine environment
- COLREGS
- collision avoidance
- collision risk
- marine navigation
- machine learning for marine environments
- object classification at sea
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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COLREGs-based Situation Awareness for Marine Vessels - a Discrete Event Systems Approach
Hansen, P. N., Papageorgiou, D., Blanke, M., Galeazzi, R., Lûtzen, M., Mogensen, J., Bennedsen, M. & Hansen, D., 2021, In: IFAC-PapersOnLine. 53, 2, p. 14501-14508Research output: Contribution to journal › Conference article › Research › peer-review
Open AccessFile446 Downloads (Orbit) -
Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images
Schöller, F. E. T., Plenge-Feidenhans'l, M. K., Stets, J. D. & Blanke, M., 2019, In: I F A C Workshop Series. 52, 21, p. 64-71Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile646 Downloads (Orbit) -
Comparing Spectral Bands for Object Detection at Sea using Convolutional Neural Networks: Paper
Stets, J. D., Schöller, F. E. T., Plenge-Feidenhans’l, M. K., Andersen, R. H., Hansen, S. & Blanke, M., 2019, In: Journal of Physics: Conference Series (Online). 1357, 1, 12 p., 012036.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile159 Downloads (Orbit)
Projects
- 1 Finished
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ShippingLab Autonomy
Blanke, M. (PI), Galeazzi, R. (CoPI), Dittmann, K. (CoPI), Hansen, S. (CoPI), Papageorgiou, D. (Supervisor), Nalpantidis, L. (Supervisor), Schöller, F. E. T. S. (PhD Student), Plenge-Feidenhans'l, M. K. (PhD Student), Hansen, N. (PhD Student), Andersen, R. H. (Project Participant), Becktor, J. B. (PhD Student), Enevoldsen, T. T. (PhD Student), Dagdilelis, D. (PhD Student), Karstensen, P. I. H. (Project Participant), Nielsen, R. E. (Project Participant), Garde, J. (Project Participant), Ravn, O. (Supervisor), Christin, L. P. E. (PI) & Nielsen, R. E. (Project Participant)
01/04/2019 → 31/12/2022
Project: Research