Master Thesis: Drone Coordination for Multi-Coverage of Environments

Dronehub K
Jan 31 · 2 min read

Context

Karl-Popper-Kolleg on Networked Autonomous Aerial Vehicles (KPK NAV) is a research group focused on real-world applications, in this case to realize the 3D reconstruction of a real environment using collaborative autonomous vehicles. Among four research areas, which are navigation and pose estimation, coordination and mission planning, wireless communications and networking, and distributed autonomous network synchronization, two master thesis topics are derived.

Description

The student will get a chance to work with the real drones, learn how to program, control, and coordinate them. The environment will be placed indoor, together with the motion tracking system able to give locations of the corresponding entities in the system in submillimeter accuracy. There will be two drones used to accomplish the mission.

There are several challenges a student will have to handle in order to successfully understand and complete the project. One of them is how to navigate both drones through the environment, avoiding obstacles and collisions. Moreover, the main problem is described as the multi-coverage problem, which assumes that every visible surface in the environment should be covered by multiple drones (in this case two). Therefore, the student will have to take into account the object’s geometry, cameras’ specifications and other requirements in order to compute the viewpoints for each surface. When reached the points on the path, they should take images of the surface from that location. The object is considered covered when all visible surfaces have been covered by the stereo pair of drones.

Tasks

  1. Setup the Intel Aero drone for indoor flights
  2. Perform literature research on waypoint navigation algorithms
  3. Deploy a single drone waypoint-following algorithm based on position information from a motion capturing system
  4. Implement a two drone waypoint-following algorithm (e.g. by deploying an existing leader-follower algorithm)
  5. Assess the implemented algorithms

Milestones

M1: Drone setup and literature study

M2: Algorithm implementation

M3: Experimental evaluation

Requirements

  • Good knowledge in Python/C++, Linux
  • A plus is the knowledge of Robot Operating System (ROS), GIT
  • English (thesis has to be written in English)
  • Interest for new software and hardware aspects of drone research

Period and Contacts

Time period: 6 months, beginning SS19

Internal supervisor(s):

- Bernhard Rinner (bernhard.rinner@aau.at)

- Petra Mazdin (petra.mazdin@aau.at)

External supervisor(s): n/a

Skills

Theory [◆◆]

Experiments [◆◆◆]

Implementation [◆◆◆]

Dronehub K

A multidisciplinary team at University of Klagenfurt and Lakeside Labs performs research on networked autonomous aerial systems.

Dronehub K

Written by

Dronehub K

A multidisciplinary team at University of Klagenfurt and Lakeside Labs performs research on networked autonomous aerial systems.

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