Complex Flight Planning Tool

Aerospace Machine Learning

We implemented a complex flight planning tool which computes optimal aircraft routes over various geographical objects while taking into account fueling time and fuel consumption, number of aircrafts in a fleet, no-fly zones and other special conditions.

Our tool benefits from parallel execution and optimized instructions and performs cutting-edge data preprocessing that helps normalize heterogeneous objects, including density-based clustering of closely located objects, recursive decomposition of large objects and a few other computational geometry algorithms. It also queries the Google Geolocation API in order to generate precise coordinates of a computed flight.

Back-End

Mathematics

C++
Python
Numpy
Scikit-learn
CGAL
Google OR-Tools
Google Geolocation API
Cython
Scipy
Jupyter
Shapely
DBSCAN
Docker

Normalization of heterogeneous objects
Recursive decomposition of objects
Density-based clustering
Computational geometry

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