Tanguy Gerniers

- Lead engineer for key R&D projects including mission-level UAS autonomy, GNSS-denied navigation, and Starlink C2 connectivity. - Primary R&D flight test engineer and flight test pilot; responsible for planning and executing flight test campaigns for new features, configurations and applications. - Project manager for the 3-year Südtirol mountain shelter drone resupply pilot project; overcoming the technological, regulatory, and operational challenges of transforming the alpine logistics industry. - Pilot instructor with over 300 logged flights on heavy-lift UAS ranging from 178kg to 350kg MTOM.

Researched high-altitude vision-based navigation capabilities for future Mars rotorcraft as part of my master's thesis with the Robotic Aerial Mobility group (347T) under the supervision of Univ.Prof. Dr. Roland Brockers: - Demonstrated the limitations and failure modes of SOTA vision-based navigation capabilities at high-altitudes and over rough Martian terrain using a custom-built Gazebo-PX4-ROS SIL flight simulator, providing key insights into which flight parameters, filtering methods, and sensor configurations minimize position drift and uncertainty. - Implemented a novel initialization method for the inverse-depth of SLAM features in RVIO, achieving 6% less position error and 1.6% less state uncertainty than the SOTA for flights of over 100m in altitude in outdoor flight experiments. - Ported a MATLAB-based flight simulation framework to C++, improving simulation speed by a factor of 32 using multithreading, alongside other usability and data visualization upgrades. - Developed Lidar sensor drivers in C, 3D Mars environments (incl. terrain assets) using Blender, UAV component CADs using SolidWorks, and flight test plans for indoor and outdoor flight experiments.

Researched long-duration autonomous navigation capabilities for aerial robots as part of the Control of Networked Systems group (CNS) under the supervision of Univ.Prof. Dr. Stephan Weiss: - Extended the CNS autonomy engine (https://github.com/aau-cns/autonomy_engine) with autonomous battery-charging capabilities for long-duration tasks. - Developed a vision-based precision-landing algorithm in C++ to guide the UAV towards its charging station using ArUco markers. - Evaluated the precision-landing capabilities of our UAV through repeated indoor experiments using PX4 and an OptiTrack motion capture system. - Integrated an NVIDIA Jetson Orin and a VOXL2 UAS autopilot with SkiffOS (https://github.com/skiffos/SkiffOS), the CNS flightstack (https://github.com/aau-cns/flight_stack), and a range-visual-inertial sensor suite.

Developed technical training courses covering power and sensor systems and their applications, including high-reliability components for aerospace, wireless charging technologies, and widebandgap principles.

Researched anomaly detection and graph optimization deep learning methods as part of the Transportation Informatics Group (TiG) under the supervision of Univ.-Prof. Dr.-Ing. Kyandoghere Kyamakya: - Performed literature reviews on the topics of anomaly detection and graph optimization using deep learning methods. - Evaluated various deep-learning algorithms for the graph optimization of transportation networks in developing countries.

Branch of study : Business Engineering Specialization : Robotics and Autonomous Systems Master thesis : High-altitude vision-based navigation for future Mars rotorcraft @ NASA JPL

Branch of study : Information Technology Bachelor thesis : MediDrone - Drones as a Service (DaaS) in the medical sector