lunduniversity.lu.se

Automatic Control

Faculty of Engineering, LTH

Automatic Control  

LTH best Master's thesis

Johanna Wilroth has been awarded the prize "LTHs Jubileumsstipendium 2020" for the best master thesis at LTH. In her thesis "Domain Adaptation for Attention Steering," Wilroth studied novel algorithms for improving the performance of hearing aids using EEG signals in combination with auditory input. The master thesis was done at the Department of Automatic Control, with Carolina Bergeling as main supervisor, in collaboration with the research center Eriksholm/Oticon in Denmark. The collaboration also involved the departments of Psychology and Mathematical Statistics at Lund University.

Paper Award

We congratulate Marcus Greiff (to the right in the photo) and co-authors Anders Robertsson (left) and Karl Berntorp (middle) who was awarded the 2020 IEEE CCTA Best Student Paper Award for the paper "MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering”.

 

M.Sc by Sond & Johnsson: Controlling a sliding contact on an electric vehicle with computer vision and AI

Seminarium

From: 2021-01-28 13:15 to 13:45
Place: https://lu-se.zoom.us/j/65008592425
Contact: johan [dot] gronqvist [at] control [dot] lth [dot] se
Save event to your calendar


Controlling a sliding contact on an electric vehicle with computer vision and AI

Abstract:

Emission from vehicles is a massive problem and contributes to the climate change on our planet. One solution that people are turning to is electrical propulsion instead of fossil fuel. There are however problems with putting big batteries on vehicles. They are expensive to build, require rare minerals and the process of creating batteries emits plenty of greenhouse gas. To reduce the need of big batteries Elonroad is creating a way of charging ground vehicles while driving. This works by putting rails in roads and sliding contacts underneath vehicles. For this to work the sliding contacts and the rail needs to stay aligned while driving. In this thesis the problem is solved by controlling the sliding contacts position with use of cameras and machine learning. The proposed structure is to use a pre-trained neural network called \textit{MobileNet} together with a custom neural network to estimate the position of the sliding contact. The estimated position is then used as input to a PID controller that controls the position of the sliding contact with a motor.


Students: 

Axel Sondh

Björn Johnsson

Supervisors:

Johan Grönqvist*, Dept. of Automatic Control, LTH

Dan Zethraeus, Elonroad AB

Examiner:

Kristian Soltesz

 

* Contact person


The presentation will be held using Zoom: https://lu-se.zoom.us/j/65008592425



Recent Publications

Welcome to KC4!

In May, we moved to temporary offices at KC4 in Kemicentrum. Here we will stay for two years while the M-building is being renovated. See "contact" (in About) for the new visiting adress.

Formula Student

The department has a collaboration with the Lund Formula Student team, who are developing a fully autonomous race car to compete in Formula Student events all across Europe. They are always looking for interested and talented team members. If you are interested in a wide variety of technologies such as neural networks, control theory, ROS, etc, please visit their website or contact them at: technical.driverless@lundformulastudent.se.