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Master thesis – Graph Neural Networks in 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 warehouse specifications. The network must handle intended traffic loads without traffic jams, verified by simulations. However, 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 traffic scores for 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. Opportunities may also exist 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 performance on unseen data. The work will be carried out as follows:
- Literature review to identify relevant Graph Neural Network models.
- Implement and train relevant models on existing data sets using Python libraries such as NumPy, Pandas, Matplotlib, and PyTorch.
- Compare the results from different models and provide recommendations on model selection.
- Generate new data sets. You will be responsible for selecting suitable scenarios in dialogue with domain experts at Kollmorgen and using various existing Python scripts to generate synthetic data, including labeling.
- Repeat training and analysis on new data sets, focusing 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 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.
