En säker landning av drönare via ett artificiellt neuralt nätverk
Typ
Examensarbete på grundnivå
Program
Publicerad
2019
Författare
Hyttring, Adam
Gustafsson, Hannes
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Modern drones are often supplied with a built-in landing function. The function is usually
primitive and rarely involves any advanced risk assessment. In this report, we propose a
solution to transfer the human component of the risk assessment to the drone in the form
of a neural network.
It has previously been found advantageous to make valuable information more explicit
before using it as an input in a neural network. Two different types of network models
are compared in the work. One network only uses the drone’s camera frames as input
variables, reducing its complexity. The second network performs more transformations on
the drone’s camera frames to first extract valuable information from the images to make
them explicit. The network then uses the explicit information in combination with the
drone’s frames as input variables. In this report, a broader comparison is also presented
about what impact on the network’s precision more explicit data has.
The result of the work shows that networks that use explicit, valuable information have
higher precision, but that the magnitude of the precision difference depends on the properties
of the task. We also show that a neural network can, with good precision, perform
a risk assessment of a drone’s landing.
Beskrivning
Ämne/nyckelord
Artificial Intelligence , Automatic landing , Neural network , Explicit/Implicit information , Drones