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Master Thesis in Graph Neural Networks for AGV System Design
1 month ago
Background
An Automated Guided Vehicle (AGV) system is a fleet of mobile robots that transport goods in a network of virtual roads, designed according to the warehouse specification. The network must handle the intended traffic load without traffic jams, verified by simulations. However, such simulations are time-consuming, and the design process is iterative, requiring faster feedback to decrease costs. 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 task will be to continue the work of a previous master thesis, which used Graph Neural Networks to predict the traffic score of an intersection. 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 explore 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-parameter choices affect the performance on unseen data.
About You
This thesis project requires a solid background in mathematics, statistics, and programming, and we are 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.
Additional Information
We will provide computers and 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 accepted by the university and presented at Kollmorgen.
