PolarRoute

PolarRoute

Start date
1 March, 2023
End date
1 March, 2033

Project Summary:

PolarRoute, a sub-project within Autonomous Marine Operations Planning,  is developing a integrating AI algorithm that utilises a diverse range of available environmental datasets, and forecasts, to provide real time navigational routes for ship or autonomous vehicles. This toolkit will allow the Captain decision support for route-planning whilst on the bridge of the SDA. These planned routes will be displayed via digital dashboards that enable proposed courses and parameter settings to be visualised in tandem with existing information services on the bridge. In addition to running on the ship this toolkit can be run back in the office for operations support, with its transferable nature allowing it to be applied to other NERC vessels and autonomous vehicles with minor changes in vessel information.

BAS has committed to achieve Net Zero carbon emissions by 2040, aligning with NERC and UKRI sustainability commitments. Part of the BAS implementation plan comprises the reduction of shipping emissions by developing fuel and time efficient routes that the Captain can use to minimise SDA fuel usage.  To achieve these goals the PolarRoute is leveraging Machine Learning-based sea ice/weather forecasts in order to provide both long horizon seasonal route-planning and real time route updates. The toolkit will provide the capability for the Captain to compare and evaluate different possible routes between destinations in terms of their fuel and carbon costs.

The route-planning functionality will comprise a fundamental component of a Digital Twin for the navigation of the SDA which will both contribute to, and benefit from, other ongoing Digital Twin projects at BAS.

The project will provide initial demonstrators of the key components:

  • Phase 1 – ending March 2023
    • To provide a toolkit for automated decision-making tool to optimise passage planning and overall carbon efficiency.
    • Route planning within sea-ice predictions from IceNet machine learning forecasts.
  • Phase 2 – ending March 2025
    • Integration of toolkit onboard the SDA, to allow devision-support for ship Captain.
    • A risk-aware navigation system supporting route-planning in complex ice conditions.
    • Deployment of the route-planning capability on the marine autonomous vehicles

In the following we present a sequence of interactive plots that show the current capability of the route planner. The intention is for viewers to click through the interactive layers to explore the different features currently available.

Examples:

Outlined below is a series of interactive examples of the work we are currently doing. Clicking the picture will take you to the individual interactive plots.

Example 1 – In this example we compare the information content of a mesh of Sea Ice Concentration (SIC) relative to the raw data. Try turning off and on the separate layers in the animation, found on the right of the plot, and zooming around the region. The greyed-out region represents land, and regions coloured in purple show the percentage of sea ice concentration, with darker purple a higher sea-ice concentration. Please click picture to go to interactive plot.\

Map

Example 2 – In this example we demonstrate the maximum ship speed and fuel usage across the region as two additional layers in the original interactive plot. In areas where the sea ice is too dense for transit are represented by an additional layer of ‘Extreme Sea Ice’, a region of no-go for the ship. Try turning on the separate layers and see how the sea ice concentration affects each of the vehicle specific layers. Please click picture to go to interactive plot.

Map, radar chart

Example 3 – In this example we demonstrate the routes from a location close to the BAS research station ‘Rothera’. In this example we show a series of travel-time optimized and fuel optimised routes from this location to a collection of points of interest, providing the travel-time and fuel usage of the routes in separate layers in the interactive plot. Try clicking on the lines and compare the travel-time and fuel usage differs for the fuel and travel time objective functions. Please click picture to go to interactive plot.

Diagram

 

Publications

Jonathan D. Smith, Samuel Hall, George Coombs, James Byrne, Michael A. S. Thorne,  J. Alexander Brearley, Derek Long, Michael Meredith, Maria Fox,  (2022), Autonomous Passage Planning for a Polar Vessel, arXiv, https://arxiv.org/abs/2209.02389

Jonathan D. Smith, Samuel Hall, George Coombs, Harrison Abbot, Ayat Fekry, Michael A. S. Thorne, Maria Fox, Derek Long,  (2023), Path-Planning on a Spherical Surface with Disturbances and
Exclusion Zones, in Review, Journal of Artificial Intelligence Research