Introduction Current interplanetary surface exploration depends on human teleoperation of robots, which helps ensure the rover’s safety, but also limits operations since direct line of sight with communications equipment on Earth is needed. This may limit the amount of the surface of the planetary body the rover can cover over Read more…
To realize the full power of machine learning, organizations need to focus on operations and delivery of the predictions as much as the development of the algorithm itself. In the case of NASA and ATD-2, building trust in the machine learning predictions is essential to expanding their use to improve inefficient operations and unused capacity. Air traffic controllers must trust the recommendations presented by them, and validation is essential towards building trust.
The taxi-out time predictions help pilots decide when to “single-engine taxi”: taxi most of the way to the runway with only a single-engine turned on, turning on the second engine just a few minutes before take-off. The single-engine taxi decision is typically made by pilots within an hour of push back. Still, our customer asked for predictions up to four hours in advance of the expected push back time.
Use of the Cloud has allowed commercial and Government systems to leverage the flexibility, scalability, reliability, and security of Cloud infrastructure to achieve significant efficiencies, and rapid innovation, in complex operational environments. The associated move to a micro-services Service Oriented Architecture (SOA) in the Cloud has further enhanced modularity, re-use of system capabilities, and simplification of verification and validation testing.
Addressing Inefficiency in Today’s Air Transportation System According to NASA1, many of today’s air transportation system issues can be attributed to a lack of information sharing amongst the operators responsible for managing air traffic in busy terminal environments. Concepts and technologies to improve the arrival, departure, and airport surface traffic Read more…
The FAA needed an aviation technology partner that had substantial expertise in fusing data streams like this. The FAA turned to Mosaic ATM, a leading aviation technology company, to build this data integration capability. Mosaic developed a data exchange system to assist the FAA SWIM program in delivering the correct information, to the right users, at the right time.
As part of the ATD-2 effort, Mosaic, in conjunction with NASA, evaluated an ML-driven approach against the controller-derived assignments, hypothesizing that the performance would be equivalent to that of an expert air traffic controller.
This case study examines how Mosaic helped NASA and the FAA dynamically balance airspace capacity & demand.
We built a predictive machine learning model that incorporates weather forecasts and air traffic movements to provide decision support to air traffic controllers.
Mosaic built custom ML & AI models for a leading express shipping company to optimize their overnight shipping operations.