Bachelor's or Master's Thesis: Intelligent Signal Processing and Sampling Strategies for Gas Source Estimation in Intelligent Robotic Olfaction

05.02.2025, Abschlussarbeiten, Bachelor- und Masterarbeiten

Robotic olfaction is a fascinating yet underdeveloped field compared to well-established areas like computer vision or acoustics. Despite its immense potential for applications in environmental monitoring, industrial safety, and disaster response, the development of reliable and efficient robotic olfaction systems remains a significant challenge.

This thesis will focus on addressing research questions about intelligent robot olfaction. Robot olfaction problems, such as Gas Distribution Mapping and Gas Source Localization, are challenging in real-world applications, requiring robust methods to handle noisy and sparse sensor data in dynamic environments. In this thesis, the research will involve developing and implementing methods that can efficiently process temporal-spatial sensor data, fuse heterogeneous inputs (e.g., gas concentration and wind speed), and make probabilistic estimations of gas source locations.

Possible Research Directions

Depending on the student’s interests, we will support exploration of one or more of the following topics:

• Sensor Signal Processing: Develop algorithms for online calibration, drift compensation, or gas sensor modeling to improve the utility of gas sensor measurements.
• Gas Source and Distribution Estimation: Design or adapt algorithms (e.g., Bayesian inference, Gaussian processes) to estimate gas source locations or map gas distributions from sparse and noisy sensor measurements.
• Adaptive Sampling Strategies: Investigate intelligent sampling methods (e.g., active learning, reinforcement learning) to optimize the selection of measurement points, enabling efficient and accurate gas source localization.

Prerequisites

• Excellent programming skills in Python
• Preferable experience in PyTorch or TensorFlow
• Background in Informatics/Robotics (CIT School), Applied Mathematics, or Physics
• Independent and self-motivated working

If you are interested, please send an email to han.fan@tum.de with a short CV and your grade report.

Kontakt: han.fan@tum.de