In façade construction, the installation of anchors and large prefabricated panels is a critical and delicate process. Anchors require precise insertion or controlled contact with structural surfaces, while panels are heavy, wide, and sensitive to misalignment. The AMALTEA project aims to address these challenges through the integration of robotics and automation. However, both anchor installation, as a contact-rich task, and panel placement, involving crane–robot interaction, require compliant robot behavior to handle uncertainties and external disturbances. This motivates the integration and development of low-level compliant control strategies for safe and robust robotic manipulation.

Within this research activity, the candidate is expected to integrate, test, and validate suitable compliant control strategies in relevant simulation and experimental scenarios.

Objectives:

Analyze state-of-the-art low-level compliant control strategies (e.g., Cartesian impedance/admittance control).
Develop and integrate compliant control approaches tailored to application-specific requirements:
Contact-rich anchor installation.
Panel alignment through robot–crane collaboration.
Ensure full compatibility and integration within ROS 2.
Validate the approach in MuJoCo and on a real robotic system.
Master Thesis will be conducted and written in English.

Knowledge Gained:

Acquire practical experience in implementing and evaluating compliant control strategies within a ROS 2-based framework.
Validate system performance through simulation and real-world experiments.
Contribute to ongoing research in robotic construction within an international and interdisciplinary European project.

Requirements:

Currently enrolled in a Master’s program in Mechanical Engineering, Computer Science, Robotics, Electrical Engineering, or a related technical field
Basic programming skills in Python and preferably C++
Familiarity with ROS 2 and MuJoCo
Familiarity with Git for version control and Docker
Solid understanding of robot kinematics, dynamics, and sensors
Experience with CAD software (e.g., Inventor, SolidWorks)
Fluent in English

Planned start: by arrangement

Supervisors: Jun.-Prof. Dr.-Ing. Kepa Iturralde, Vincenzo Orlando M.Sc.

If you’re interested, please send your CV and academic transcripts to the following email address:

vincenzo.orlando@ibl.uni-stuttgart.de

Jobs bei der Universität Stuttgart

Jobangebote