Maciej Chociej
Papers
1
Total Citations
2
H-Index
1
About
Maciej Chociej is a researcher at the intersection of aerial robotics, control systems, and machine learning, with a focus on enabling safe, efficient flight in challenging outdoor environments. His most notable contribution is the development of a learning-based air data system for fixed-wing aerial vehicles, which integrates Extended Kalman Filtering with autoregressive feedforward neural networks. This system allows drones to estimate aerodynamic forces using only IMU and GPS data—eliminating the need for expensive or fragile pitot-static probes. By doing so, Chociej’s work directly addresses a critical bottleneck in high-speed autonomous flight, where accurate airspeed and angle-of-attack estimation are essential for stability and control. While his citation count remains modest, the practical significance of his approach is evident: it offers a robust, sensor-light solution for aerial robots operating in real-world missions, from package delivery to environmental monitoring. Chociej’s research exemplifies how data-driven methods can replace traditional hardware, pushing the boundaries of what small autonomous aircraft can achieve without sacrificing safety or performance.
Research Focus
Key Achievements
Top Papers
- 1