Complex Flight Planning Tool
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.
- 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