[Robotics] Master’s Thesis: Intelligent Sampling Strategy for Open Path Gas Sensing with Mobile Robots Using Reinforcement Learning

16.01.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

This Master's thesis is supervised by the chair "Perception for Intelligent Systems"

Gas leaks in industry or nature can harm humans, animals, and infrastructure. Finding the sources of an invisible, potentially hazardous gas can be even more dangerous for human workers. So, it sounds like a perfect job for robots! To avoid that the robot needs to get in touch with the gas, open-path laser-based sensors are the means of choice. These systems allow us to remotely measure the gas concentration between two robots.

Your Mission is to develop a strategy for navigating the robots to take remote measurements. This needs to be done in an intelligent way such that we can determine the locations of gas as fast as possible. Therefore, you will train the multi-agent system with reinforcement learning algorithms. To evaluate the sampling performance of your trained agents, you will compare it with other methods in terms of time and efficiency.

Prerequisites:
- Excellent programming skills in Python
- Preferable experience in PyTorch or TensorFlow
- Background in Informatics or Robotics (CIT School)
- Independent and self-motivated working

If you are interested, just send an email to marius.schaab(at)tum.de and thomas.wiedemann(at)tum.de with a short CV and your grade report.

Kontakt: marius.schaab@tum.de, thomas.wiedemann@tum.de