We are consistently searching for motivated Students from Computer Science, Robotics, Electrical Engineering, and similar to work with us. We can offer topics suitable for a Bachelor's or Master's Thesis and internships or student jobs (Werkstudententätigkeit).
If you're interested and would like more information, please don't hesitate to contact Prof. Lilienthal or any chair member.
Master’s Thesis: Intelligent Sampling Strategy for Open Path Gas Sensing with Mobile Robots Using Reinforcement Learning
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.
Master’s Thesis: Multi-Agent Formation Planning for Gas Detection
Mobile robots or drones equipped with appropriate sensors are perfect platforms for sampling toxic or dangerous airborne trace substances or gases to avoid threads for human operators. In emergency scenarios, deploying multiple agents to reduce response times is further advantageous.
The goal of the thesis is to plan optimal formations and paths for a multi-agent system to increase the chance of detecting gas emitted from a source at an unknown location. The planning should incorporate environmental parameters, like wind, and model assumption of gas propagation in air.
Requirements:
- Excellent programming skills in Python
- Good knowledge of numerical methods (FEM, parameter estimation, optimization)
- Self-motivated working and a good working knowledge of English or German
- Student at CIT
The Master’s Thesis might be carried out in collaboration with the Swarm Exploration Group at the Institute of Communications and Navigation at the German Aerospace Center (DLR) in Oberpfaffenhofen.
If you are interested, please send an email to thomas.wiedemann@tum.de with a short CV and your grade report.
Master’s Thesis: Visual Tracking of a UAV for Open-Path Measurements
Tunable Diode Laser Absorption Spectroscopy (TDLAS) allows us to infer the gas concentration with a laser by measuring the amount of absorbed energy along the laser path. Our goal is to integrate this measurement concept into a robotic system. Therefore, a robot will be equipped with the TDLAS sensor and a flying UAV with a retro-reflector that reflects the laser beam to the sensor. This allows us to measure the gas concentration in the air between the robot and the UAV.
To this end, a crucial task is to align the laser towards the reflector mounted on the UAV. The goal of the thesis is to develop a controller for a pan-tilt unit carrying the laser. The pan-tilt unit must direct the laser to the UAV and track it over time. The planned approach is to detect and track the UAV using a visual camera.
Requirements:
- Excellent programming skills in Python or C++
- Preferable background in control theory
- Very strong interest in implementation tasks in combination with hardware (single board computer)
- Experience in image processing/OpenCV
- Self-motivated working and a good working knowledge of English or German
- Student at CIT
The Master’s Thesis might be carried out in collaboration with the Swarm Exploration Group at the Institute of Communications and Navigation at the German Aerospace Center (DLR) in Oberpfaffenhofen.
If you are interested, please send an email to thomas.wiedemann@tum.de with a short CV and your grade report.
Bachelor's or Master's Thesis: Intelligent Signal Processing and Sampling Strategies for Gas Source Estimation in Robotic Applications
This thesis aims to explore advanced signal processing and data fusion for gas source localization (GSL) in robotic olfaction. The focus is on developing efficient algorithms to handle noisy, sparse sensor data. Candidates will gain hands-on experience in data processing, probabilistic modeling, and experimental validation.
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(at)tum.de with a short CV and your grade report.
Master’s Thesis: Multi-Agent Path Planning for LiDAR Acquisition
In emergent search and rescue situations, time is of the essence. Systematic path optimization is essential. Laser scanners are a proven tool to gather 3D information about ground surfaces. Drones can be used to acquire essential data for mapping. By leaning on these compatible technologies, an optimized approach can be derived. Our goal is to find the most optimal path for a swarm of drones to gather necessary data in a search and rescue scenario.
Your task will be to investigate state-of-the-art path planning algorithms for a swarm of drones with the purpose of acquiring point cloud data. The solutions should be evaluated in simulations and in-situ.
Requirements:
- Strong proficiency in Python programming; experience with ROS/ROS2 is a valuable advantage
- Hands-on experience with or a keen interest in hardware implementations
- Familiarity with point cloud data and image processing
- Ability to work independently with a self-motivated approach
- Fluent in English
This thesis is in collaboration with the German Aerospace Center (DLR). If you are interested, please send an email to Sigrid.strand(at)dlr.de with a short CV and transcript.
Working Student (Part-Time) for Implementing LiDAR Sensors on a Drone Swarm
Are you passionate about cutting-edge drone technology and eager to make an impact?
Join our swarm exploration team at DLR as a working student and contribute to integrating LiDAR sensors into a swarm of drones. This is your chance to gain hands-on experience in robotics and sensor integration, while working on real-world applications that push the boundaries of autonomous systems.
As part of our development team, you will assist in implementing and optimizing LiDAR systems on drone swarms, enabling precise navigation, obstacle detection, and forest mapping. You’ll work on tackling both software and hardware challenges. Tasks include:
- Designing and integrating LiDAR sensor mounts and configurations for drones.
- Developing and testing algorithms for sensor data processing.
- Assisting with field tests to validate LiDAR performance in swarm operations.
- Documenting findings, results, and improvements for future development.
Requirements:
- Enrollment in a relevant field of study: Robotics, Computer Science, Electrical Engineering, or similar.
- Familiarity with LiDAR technology, sensor integration, and data processing.
- Strong proficiency in Python programming; experience with ROS/ROS2 is a valuable advantage
- Knowledge of drone systems and related communication protocols is a plus.
- Ability to work independently with a self-motivated approach
- Availability for at least 10 -18 hours per week.
- Fluent in English
If you are interested, please send an email to Sigrid.strand(at)dlr.de with a short CV and transcript.
Master’s Thesis: Reliable Sampling Rate for AI-Based Eye-Tracking Analysis
Eye tracking is a powerful tool for understanding human attention, cognition, and behavior, with applications in psychology, education, and human-computer interaction. The sampling rate is a crucial factor in determining the quality of eye-tracking data. Since eye muscles are among the fastest in the human body, a reliable sampling rate is essential for capturing accurate eye movement data. If the sampling rate is too low, critical details may be lost, negatively affecting downstream AI-based analysis.
This thesis will investigate how low sampling rate eye-tracking data impacts AI-driven analysis and determine the minimum sampling rate required for reliable and valid results. Your mission will be to develop a pipeline to determine the reliable sampling rate required for different eye-tracking applications. This pipeline will ensure that data quality is maintained for AI-driven human analysis. A specific focus will be placed on estimating the necessary sampling rate for mathematics education scenarios, where precise eye movement tracking is crucial for understanding learning behavior.
While the initial focus is on these areas, we remain open to exploring other relevant topics where eye-tracking data quality and sampling rate play a critical role. The research may evolve based on new challenges, emerging methodologies, or interdisciplinary insights.
Prerequisites:
- Strong programming skills in Python
- Background in AI or data analysis
- Interest in eye-tracking and human-computer interaction
- Student at CIT
- Independent and self-motivated working
If interested, just email parviz.asghari(at)tum.de with a short CV and your grade report.
