ROS | Mobile Robotics Research Group — Prof. Dr. Christian Pfitzner

Markerless Indoor Localization of a Drone without GNSS

Mon, 15 Jun 2026 00:00:00 +0000

Description

The drone used at the AerodrOHM currently determines its position via an external, marker-based tracking system. This provides very accurate poses but requires a permanently installed, instrumented flight space. The goal of this thesis is a markerless, GNSS-free indoor localization based solely on onboard sensors, so that the drone can fly independently of the external tracking. The work builds on the existing drone and the established ROS system architecture.

Work Packages

Familiarization with the existing drone platform and the marker-based tracking system at the AerodrOHM
Selection and integration of suitable onboard sensors (camera, IMU, optionally optical-flow or depth sensor)
Implementation of a markerless, GNSS-free localization method (e.g. visual-inertial odometry or visual SLAM)
Integration into the existing ROS system architecture and flight control
Evaluation of accuracy and robustness against the marker-based tracking as ground truth
Flight experiments and documentation of the results at the AerodrOHM

Requirements

Programming skills (Python and/or C++)
Ideally some experience with ROS / ROS 2
Basic knowledge of computer vision and/or state estimation (e.g. Kalman filter)
Interest in drones, sensor integration and localization
Independent and diligent way of working

This topic can be completed as a project or bachelor’s thesis subject to agreement.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de

Human-Robot Interaction on the UR12e – Tic-Tac-Toe as an Interactive Demo

Fri, 12 Jun 2026 00:00:00 +0000

Motivation

Collaborative robot arms such as the UR12e make robotics immediately tangible — especially when people can interact with them directly. A game like Tic-Tac-Toe is ideal for this: everyone knows the rules, the outcome is easy to follow, and the robot demonstrates perception, decision-making and precise motion in a single, illustrative sequence.

At the TTZ Nürnberger Land, this is to become the basis for a demonstrator that makes robotics graspable for a broad audience — at family events, open-house days and for visitor groups. At the same time, the project serves as a platform to put concepts of safe human-robot interaction into practice.

Work Packages

Perception

Camera-based detection of the board and the placed game pieces
Calibration between camera and robot coordinate frames (hand-eye calibration)

Game Logic

Implementation of the Tic-Tac-Toe logic (e.g. minimax) for move computation
Detection of game end, win, loss and draw

Robot Control

Control of the UR12e via its existing interfaces (ROS or URScript)
Grasping and safely placing the game pieces
Functional safety in collaborative operation (force/speed limiting)

Interaction & Demo

Intuitive game flow and user guidance understandable for non-experts
Robustness testing and preparation as a presentable demo
Optional outlook: conceptual extension to chess

Requirements

Programming skills in Python and/or C++
Ideally some prior exposure to robotics
Interest in computer vision, game logic and human-robot interaction
Enthusiasm for hands-on work with real robot hardware

This topic is offered as a project or bachelor’s thesis and, given a good fit, can serve as a foundation for follow-up work (e.g. extension to chess).

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de
Co-Supervisor	M. Sc. Patrick Fußy	patrick.fussy@th-nuernberg.de

Location: TTZ Nürnberger Land / TH Nürnberg Georg Simon Ohm

Recommissioning of a Mobile C-Arm Robot with Mecanum Drive

Tue, 09 Jun 2026 00:00:00 +0000

Work Packages

Analysis and documentation of the existing robotic system
Review and evaluation of hardware and software components
Troubleshooting and development of a recommissioning concept
Implementation or adaptation of the mecanum drive control
Minor mechanical modifications and integration work
Testing, validation, and documentation of system functionality

Requirements

Interest in mobile robotics, mechatronics, and technical systems
Basic knowledge of robotics, control engineering, or vehicle systems
Practical experience in at least one of the following areas: programming, electronics, or CAD / mechanical design
Ideally, first experience with ROS, Python, or C++
Independent and structured way of working

Depending on the specific focus, the topic can be carried out as a project thesis, bachelor’s thesis, or master’s thesis.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de

Location: TH Nürnberg Georg Simon Ohm / OIC

2R Robot Arm Lab Experiment – Gravity Compensation and Reinforcement Learning

Mon, 08 Jun 2026 00:00:00 +0000

Motivation

The “Intelligent Robotics” practical course in the M-IAS master’s programme currently lacks a hands-on experiment bridging classical model-based control and modern learning-based methods. A 2R robot arm with torque control offers exactly this: students first experience the physical effects of gravity on the real system, then learn how an RL agent solves the same task in a data-driven way.

Work Packages

Hardware

Selection and commissioning of BLDC motors and motor drivers (e.g. ODrive / VESC)
Mechanical design and fabrication of arm segments (CAD, 3D printing or aluminium profile)
Integration of encoders and wiring

Software & Control

Implementation of forward and inverse kinematics
Model-based gravity compensation controller (real-time torque compensation)
ROS integration: topics, services and ros2_control

Reinforcement Learning

Building a simulation environment (Gazebo or MuJoCo) for RL training
Training an agent (e.g. PPO / SAC) on a target-reaching task
Sim-to-real transfer and evaluation on the real arm

Documentation

Creating lab instructions for use in the M-IAS practical course

Requirements

Knowledge of control theory and robotics (kinematics, dynamics)
Programming skills in Python and/or C++
Ideally experience with ROS
Interest in machine learning and reinforcement learning
Enthusiasm for hands-on hardware work and prototyping

This topic can be completed as a project or master’s thesis subject to agreement.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de

Drone Swarm Obstacle Avoidance – Collision-Free Navigation of Micro-Drones

Mon, 08 Jun 2026 00:00:00 +0000

Work Packages

Integration and calibration of the external 3D camera
Processing, filtering and transformation of point cloud data
Development of a classification to distinguish between persons and drones
Design and implementation of a reactive swarm behaviour with safety zones
Test and evaluation in the flight space with real persons

Requirements

Programming skills (Python and/or C++)
Ideally some experience with ROS
Interest in sensor integration, point cloud processing and perception methods

This topic can be completed as a project or master’s thesis subject to agreement.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de