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Bachelor's Thesis

Generation of Synthetic Training Data for Sensor-Based Location Awareness


Machine learning (ML) techniques can be used to imitate basic cognitive skills of animals. An example is the sense of location awareness. It enables all higher animals to remember a discrete number of locations, to recognize where it is, and to remember how to get from one location to another. The ability to recognize its own location in a set of known locations can be replicated using ML, e.g., with artificial neural networks, in a sensor module with multiple sensors. This can be used in industrial and factory environments for transport boxes with integrated battery-powered sensor modules that are repeatedly transported through a factory along almost the same paths.

A constantly growing dataset is being maintained by the institute. These include sensor data (e.g., accelerometer, gyroscope, magnetometer, temperature, pressure, etc.) collected at each location, while travelling along three paths in the TUHH premises.

Goals of the project

This thesis aims to explore methods for generating synthetic training data to train ML models for the problem of locating transport boxes through a comparison of three paths at TUHH. To do this, the applicability of methods should be analyzed, preferred methods should be proposed, and implementations of these methods should be made in Python with libraries. The quality of the synthetic training data should also be assessed by proposed statistical evaluation metrics and by applying it to train a neural network.

Student Doguhan Harmanda
Supervisor Dr. Marcus Venzke
Nisal Hemadasa Manikku Badu