Description
Objective: Develop a prototype for a system that integrates information on sea ice conditions from a diverse set of sources, including shipboard instruments, airborne and spaceborne sensors, and sea ice model output, to yield optimized route options as a planning aid for navigation through ice-infested waters in polar regions. Description: Recent trends of warming in the Arctic have led to a steady decrease in the extent of multi-year sea ice, a corresponding increase in seasonal sea ice, and an overall lengthening of the navigable season [Refs 1, 2], thereby making the Arctic increasingly open to maritime traffic. Vessels operating in and near sea ice must make navigation decisions that balance the capabilities of the ship with the objectives of their voyage. Such route planning is complicated by the dynamic nature of sea ice, as it is subject to movements caused by a number of factors such as the Beaufort Gyre, transpolar drift, and weather events, which are even more pronounced on the thinner, seasonal ice. A system capable of aiding navigation teams in route planning based on ice observations and forecasts over time scales on the order of hours to days is essential for safe navigation through polar regions. Currently, ice navigation relies heavily on manual processes. A majority of route planning information comes from satellite imagery, either optical or synthetic aperture radar (SAR), or from forecast information from entities like the U.S. National Ice Center. Due to dynamic weather conditions and rapid movement, the operational value of overhead imagery is sometimes temporally limited. These longer-range data sources are augmented by shipboard systems, such as onboard radar systems for icebergs, in-situ ice floe, and pack ice detection, which typically have detection ranges on the order of a few tens of kilometers. These close-range systems help inform tactical navigation decisions and near-term route planning. Key aspects of ice analysis, whether conducted onboard or remotely, are ice edge definition, identification of ice types (e.g., seasonal ice, multi-year ice) and concentration, and detection of ice features such as ridges and icebergs. This analysis is then presented to the navigation team and command who assess the current and planned route and make course adjustments as necessary. Current ice forecasts do not always adequately account for projected ice movement over the next 12-96 hours, which is crucial for effective route planning. Moreover, the analysis and route planning are often separate functions, each conducted by distinct teams based on their own personal experience and knowledge. This separation can lead to suboptimal decisions and increased risk. The goal of this SBIR topic is to develop a prototype tool that helps ships make safe navigation decisions in the Arctic. The tool should leverage established ice prediction models and incorporate other available sources to assimilate models and improve forecasts. These additional sources may include: Onboard sensors: Radar, thermal cameras (forward-looking infrared), and microwave sensors on the ship. Aircraft sensors: Sensors on airplanes and unmanned aerial systems (if available). Satellites: Optical and SAR data, dynamically updated with every new overpass. Iceberg records: Historical data on where icebergs have been seen/located (e.g., from the U.S. Coast Guard's International Ice Patrol). The envisioned product is a geographic-information-system-based tool that uses artificial intelligence, first-principles algorithms, and automated data processing schemes to combine information from the above sources, update model-based predictions, provide 12–96-hour sea ice forecasts, and suggest potential navigation routes. Route options should consider vessel specifications, such as ice resistance characteristics and fuel consumption rate, and provide options for fastest route to destination, shortest route to destination, route with minimal wear/tear on vessel and crew, and maximum safe speed based on ship hull type/construction. Ultimate route decisions should be left to the vessel’s navigation team. Keywords: Polar navigation; artificial intelligence/machine learning; AI/ML; sea ice forecast; route planning; satellite imagery; data fusion; Arctic; Antarctic; ice identification; ice classification; ice prediction; ice analyst; Meteorology and Oceanography; METOC; remote sensing; modeling; shipboard sensors; human-machine interface; big data CMMC Level: Level 2 (Self)