Krzysztof Kolanowski
Papers
2
Total Citations
5
H-Index
2
About
Krzysztof Kolanowski is a researcher focused on robotics, artificial intelligence, and sensor reliability. His work addresses a critical challenge in autonomous systems: detecting sensor failures in real time to prevent catastrophic control errors. Kolanowski pioneered the use of dynamic artificial neural networks operating in parallel to identify failing signals from onboard robot sensors, a concept introduced in his 2017 paper. He later advanced this approach by integrating convolutional neural networks with k-fold cross-validation to calculate Euler angles, specifically testing his system on unmanned aerial vehicles (UAVs) like quadrocopters. This work demonstrates how neural architectures can be tailored for robust fault detection in complex, dynamic environments. Though his citation counts are modest—3 and 2 respectively—Kolanowski’s contributions are foundational in the niche of neural-network-based sensor diagnostics for robotics. His research bridges machine learning and control theory, offering practical frameworks for improving the safety and autonomy of drones and other robots. For students and researchers, Kolanowski’s work exemplifies how targeted neural network design can solve real-world engineering problems in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2