Drone Self-Monitoring

With the current diverse indoor and outdoor applications for drones in our daily life, there is a growing demand on the development of accurate self-monitoring systems to guarantee the safety of people and protect expensive assets. This project aims to improve the flight performance of the self-monitored drones using RF shadowing technology. RF shadowing has been adopted as a proven technology in several research areas for different applications, including localization, human health monitoring, agriculture applications, among others. Motivated by RF sensing advantages, we investigate the impact of fusing RF-based sensing with the other drone’s sensors on the motion performance. This fusion improves the early failure diagnoses and increases the drone’s performance in critical cases such as bad weather.

Main Contributors

Primary Research Issues

  • Electromagnetic field propagation
  • Machine learning
  • Aerodynamics modelling