Electronic Navigational Charts for Visualization, Simulation, and Autonomous Ship Control

Blindheim, Simon; Johansen, Tor Arne

doi:10.1109/access.2021.3139767

Cited by 27 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While electronic charts contain these data, there are relatively few open-source interfaces for research and development. Blindheim et al use Python and Electronic Navigation Chart (ENC), an open-source electronic chart, to create interfaces displaying water depths, islands, reefs, and shallows within a ship's navigational area, facilitating ship-route planning and optimization [33]. Zhang et al employed machine learning algorithms to analyze ship AIS data and quantified the risk of ship grounding using proximity prediction and depth information [34].…”

Section: Static Environmental Factorsmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

show abstract

Section: Static Environmental Factorsmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…To describe the execution process of the VATDC_CCRI algorithm, the AIS trajectory data of a vessel with MMSI 210698000 on 13-15 April 2019 are used as an example for detailed illustration, and the shape of the vessel's trajectory is shown in Figure 5. Te geographic location data involved are adjusted using the Mercator projection [35], and equations ( 2)-( 5) give the calculation process of the Mercator projection.…”

Section: Vessel Trajectory Data Compression Algorithm Considering Cri...mentioning

confidence: 99%

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Zhang,

Zhou

2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.

show abstract

“…Even though this method is suitable for most cases, deducing the shape of a static obstacle to a single circle can be a waste of navigable area depending on how the circular area is formulated. The SeaCharts package introduced in [17], proposes an algorithm called 'EnclosingCircles' which calculates local polygon approximation circles, based on the currently visible shoreline or depth contour corresponding to the ship draught, from any given ship position, which can be used to address this issue.…”

Section: A Static Obstacle Avoidancementioning

confidence: 99%

“…Each shapefile in the regions of interest is read individually and merged to create a single polygon. However, a more general approach would be to use the SeaCharts library [17]. For this research, we have used the shapefiles in the Norwegian Fjord Catalog from the Norwegian Mapping Authority (NCA) [18].…”

Section: A Test Setupmentioning

confidence: 99%

Model Predictive Control for Path Following and Collision-Avoidance of Autonomous Ships in Inland Waterways

Mahipala

Johansen

2023

2023 31st Mediterranean Conference on Control and Automation (MED)

Self Cite

View full text Add to dashboard Cite

While existing algorithms for open water navigation typically address path following and COLREGS compliant collision-avoidance, the unique challenges of inland waterways require a more tailored approach. We propose a two-level control strategy that employs Model Predictive Control (MPC) and Scenario-Based Model Predictive Control (SB-MPC) for path following and collision-avoidance. The algorithm proposes integrated strategies for handling riparian land, static obstacles, and dynamic obstacles. The method is tested in simulation.The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 955.768 (MSCA-ETN AUTOBarge).

show abstract

Electronic Navigational Charts for Visualization, Simulation, and Autonomous Ship Control

Cited by 27 publications

References 36 publications

A Review on Motion Prediction for Intelligent Ship Navigation

A Review on Motion Prediction for Intelligent Ship Navigation

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Model Predictive Control for Path Following and Collision-Avoidance of Autonomous Ships in Inland Waterways

Contact Info

Product

Resources

About