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Autonomous on-board drone navigation (i.e., without human intervention) in inaccessible environments is a fundamental challenge. The goal in this project is to develop novel machine learning algorithms for autonomous drone navigation in outdoor environments including localization and synchronization for BVLOS (beyond visual line of sight) scenarios and/or GPS-denied environments, by utilizing RF signals from fixed ground stations and/or in collaboration with other drones. This includes robust holistic 3D perception through sensor fusion of on-board sensors, for e.g., lidar, radar, camera, and inertial measurement units (IMUs), algorithms for detect and avoid using advanced machine learning for object detection, object classification, and ego-motion estimation. The proposed resource-constrained algorithms will be energy-efficient and robust for in-drone perception, cognition, and control.
About the project The Postdoctoral (and PhD) positions are a part of the recently funded ECSEL-H2020 project named ADACORSA (Airborne data collection on resilient system architectures), with over 50 partners across Europe. Circuits and Systems (CAS) group in the Faculty of EEMCS at TUD is one of the WP leaders (among 8) in this consortium, and will develop ground-breaking 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 BVLOS operations.
More details on the position: https://cas.tudelft.nl/Openings/vacancy.php?id=78