Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Unmanned Aerial Vehicles (UAVs) are being increasingly utilized in wide range of applications in remote sensing, situation awareness, and mobile mapping. They have the ability to carry sensors, processors, transmitters, and imaging equipment and they have unique omnidirectional maneuvering characteristics. Safe operation of UAVs requires accurate positioning and navigation systems. Present UAV positioning technology is dominated by Global Navigation Satellite Systems (GNSS) integrated with high-rate onboard inertial measurement units(IMU). Unfortunately, GNSS/IMU technology fails significantly in obstructed environments such as dense urban and indoor areas due to satellite signal blockage and IMU errors. This limitation currently prevents UAVs from being used to perform low-altitude tasks in complex obstructed environments.

To fill this gap, utilization of onboard vision/range sensors for accurate positioning and navigation has been extensively studied in the last decade. However, there are several challenges that prevent current systems from being safely adopted and commercially utilized. One major drawback is lack of robustness due to the limited ability of current sensor fusion methods to handle massive streams of heterogeneous sensors with different highly nonlinear noise characteristics in a dynamically changing environment. Another major drawback is the expensive computation. Vision/range-based integrated positioning systems have to process massive scans of data at extremely high rates to assure stable control of UAV platforms.

Small scale UAVs have additional challenges. Their truly 3D maneuvering and hovering impose several challenges on the estimation methods and real-time performance. In addition, the high vibration and electromagnetic interference exhibited by rotors cause immediate sensors errors that are difficult to model. Both accelerometers and gyroscopes suffer from large noise under high vibration and magnetometers do not work properly in close proximity to electromagnetic interference sources.

Building on the applicant’s previous work in multi-sensor positioning systems, this research program will focus on advancing the positioning and navigation capabilities of small scale UAV platforms in complex obstructed GNSS-challenging/denied environments. The research will explore the integration of artificial intelligence methods and fuzzy theory with conventional signal processing and estimation techniques to develop new motion models that are suitable for UAV dynamics and novel nonlinear adaptive sensor fusion methods that can handle heterogeneous low-cost noisy sensors. Onboard real-time processing will be optimized using Graphical-Processing-Unit “GPU”-enabled processors. The research outcomes can be readily applied in other emerging technologies such as self-driving cars, autonomous robots, and augmented reality.