[BEAMS-EE-FQ] Obstacle detection for Unmanned Aerial Vehicles using GNSS shadowing | BEAMS

[BEAMS-EE-FQ] Obstacle detection for Unmanned Aerial Vehicles using GNSS shadowing

Project information
Project type: 
Master thesis
Academic year: 
Research unit: 
Embedded Electronics
BEAMS supervisors


This thesis is done in collaboration between HIPPEROS and the embedded electronics group of the BEAMS department.

HIPPEROS is a spin-off of the BEAMS department developing real-time operating systems for embedded platforms. The HIPPEROS operating system exploits modern algorithms to better exploit available performances in such platforms to minimize costs, reduce energy consumption and provide safer autonomous systems.

Master Thesis description

The aim of this thesis is to enable real-time obstacle detection and navigation on an autonomous quadrotor. The obstacle detection principle is simple: a Global Navigation Satellite System (GNSS, e.g. GPS) provides the direction of each detected satellite and the signal-to-noise ratio (SNR) of the received signal from each satellite. When a satellite is “shadowed” by a building, the SNR drops by 10 dB, which can easily be detected from the on-board GNSS receiver. By using this principle, the UAV can then determine in which direction there are buildings and which directions are free of obstacles [1].

This project aims at validating and demonstrating an obstacle detection algorithm for a UAV based on GNSS shadowing. The on-board processor will use the HIPPEROS real-time OS. To this end, the student will have to do the following:

  • Design an obstacle detection algorithm using the GNSS receiver mounted on the quadrotor. The obstacle detection will have to satisfy real-time constraints, which is possible thanks to the HIPPEROS real-time OS;
  • Use an on-board LiDAR to design a fail-safe mechanism to avoid crashing the drone into an obstacle;
  • Design a navigation algorithm that is able to avoid the detected obstacles. The movement characteristics of the quadrotor will depend on the obstacle detection speed determined in the first point;

Supervisor(s): Prof. François Quitin, Olivier Desenfans (HIPPEROS)

Information : François Quitin (fquitin [at] ulb [dot] ac [dot] be) Tel : 02-650-2829, BEAMS Department

Students : ELEC, INFO, EM

Note: this thesis must be combined with an internship at HIPPEROS

[1] Jason T. Isaacs, C. Magee, A. Subbaraman, F. Quitin, K. Fregene, U. Madhow and J.P. Hespanha, GPS-optimal micro air vehicle navigation in degraded environments, 2014 American Control Conference (ACC 2014)

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