Contact: Dümbgen, Frederike
Synopsis: Use off-the-shelve Bluetooth angle of arrival (AoA) technology to recover the relative positions of drones in a swarm.
Level: BS, MS
Description:
Swarms of multiple cheap and light-weight drones have become a great alternative to expensive heavy-duty UAVs. Drone swarms can reconfigure on the fly to perform complex tasks requiring for example more power or coverage than drones could provide individually.
Reliable real-time pose estimation is crucial for the execution of such tasks, and even though it has been a very active research field in the past years, some crucial limitations prevail in existing solutions. Visual methods, for instance, can be extremely accurate but require high processing power and break down in feature-less scenarios (think about fog or night time). GPS in combination with magnetometers can perform well outdoors, but breaks down in non-line-of-sight conditions. RF-based distance measurements are an attractive remedy to these problems, but pose information is irrecoverably lost in this modality.
The goal of this ambitious semester or Master’s project is to explore RF-based angle measurements for drone swarm localization. In particular, the new Bluetooth 5.1 incorporates AoA measurement capabilities, making it an interesting candidate for future localization solutions. The work will consist of the design of a centralized angle-based localization algorithm (some prior work for this has been done in the lab) suitable for drones, its verification in simulation, and if time allows, deployment on real drone(s).
Deliverables: A report and a working system with clear documentation.
Prerequisites: Solid python programming skills and geometry knowledge, interest in signal processing. Ideally the student has worked with drones before or has a strong motivation to learn about them.
Type of Work: 50% algorithm design and verification in simulation, 50% experiments and evaluation.