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Master thesis – "Graph Neural Networks in AGV System Design"
4 months ago
Background
An Automated Guided Vehicle (AGV) system is a fleet of mobile robots that automatically transport goods in a network of fixed virtual roads, designed according to the specification of the warehouse. This network must have the capacity to handle the intended traffic load without traffic jams, which is verified by running simulations. However, such simulations are time consuming, and since the design process is an iterative one, faster feedback directly translates to decreased cost. In this master thesis project, you will research how Machine Learning can be used to speed up the design process by predicting the consequences of different design choices.
Goal of the thesis
Your job will be to continue the work of a previous master thesis (carried out in 2023) which used Graph Neural Networks to predict the traffic score of an intersection. In that case, the training data consisted of 50 variants of the same intersection, and the next step is to investigate how well the model can generalize when applied to larger and more diverse data sets. There might also be opportunities to look into generative models, such as Generative Adversarial Networks.
Method
The task is a form of supervised learning, and you will evaluate a range of different models on several synthetic data sets. Relevant metrics such as precision and recall should be calculated, and you will explore how model and hyper-paramater choices affects the performance on unseen data.
Roughly, the work will be carried out as follows:
Implement and train relevant models on existing data set using python libraries such as numpy, pandas, matplotlib, and pytorch.
Compare the results from the different models and give recommendation on model selection.
Generate new datasets. You will be responsible to select suitable scenarios in dialogue with domain experts at Kollmorgen, and you will use various existing python, scripts to generate synthetic data, including labeling. In other words, you will not have to annotate any data manually in this project.
Repeat training and analysis on new dataset, with a focus on how well the models generalize to unseen scenarios.
About you
This thesis project requires a solid background in mathematics, statistics, and programming, and we are therefore primarily looking for master students from engineering programs, such as Computer Science, Applied Physics, Applied Mathematics and similar. You should also have taken courses in Statistics, Machine Learning, and Neural Networks.
Number of students
2
Thesis Level
Master
Language
Thesis is to be written in English
Starting date
January 2024
Location
Kollmorgen Automation Office in Mölndal, Sweden
Additional information
We will provide computers and you will get great support from your supervisor and other colleagues
All students are compensated after the thesis is submitted and the final work meets the quality standard that is generally accepted by the university and presented at Kollmorgen.
Send in your application by submitting a CV and/or information about your studies and a short sentence about why you find this project interesting. If you get selected, the next step will be to have discussions with us to finalize the idea .
Selection takes place on an ongoing basis and might be filled, welcome with your application
Some of the good things we offer
Innovation Days every twelve weeks - a 24-hour event for the whole company to dig deeper, explore new areas and solve problemsGym at the office filled with machines for strength and fitness that is always open and free to use when you need a break in your thesis work
A bicycle storage and free car parking outside our office
Participate in our events such as monthly breakfast meetings, Day of Caring (every year we take one day to clean the west coast beaches together) etc
Good fair trade coffee (or tea) with fresh milk (every student who has been up late to study for an exam know how important coffee is)
Fresh fruit if you get hungry
Contact Information
If you have any questions about the thesis, you are welcome to contact Rasmus Åkerlund at
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