Airborne data collection on resilient system architectures (ADACORSA)
ADACORSA (Airborne data collection on resilient system architectures) will develop algorithms to realize efficient, robust, and data-fusion based cost-effective perception and control for autonomous drones. The overarching goal of this project is to provide technologies to render drones as a safe and efficient component of the mobility mix, with reliable capabilities in extended beyond visual line of sight (BVLOS) operations. Circuits and Systems (CAS) group in the Faculty of EEMCS at TUD is one of the WP leaders (among 8) in this consortium
We will develop algorithms for autonomous drone navigation including localization and synchronization for BVLOS scenarios and/or GPS-denied environments, by utilizing RF signals from ground stations and/or in collaboration with other drones.
- Sensor-fusion: Robust holistic 3D perception through sensor fusion of on-board Lidar, Radar, Camera, and inertial measurement units (IMUs).
- Detect and Avoid (DAA): Advanced machine learning algorithms for object detection, object classification, and ego-motion estimation.
- Autonomous navigation: Navigation algorithms for dynamic environments using collaborative and/or non-collaborative sensors.
- Performance: Energy-efficient, high-performance, and resource-constrained solutions for in-drone perception, cognition, and control.
Image courtesy: https://industryeurope.com/downloads/3987/download/drone%20swarm.jpg