Adaptive Component Handling for Intelligent Assembly

Mon, 07 Sep 2026 00:00:00 +0000

Motivation

Loading machines with the help of robots is still frequently carried out through rigid pick-and-place applications. The positions for grasping and placing the components are fixed. This approach is limited when assembling different variants. Deviating assembly positions of the individual variants require the robot to be adapted to the respective component.

Topic Area

Adaptability in component assembly reduces the preparation effort for positioning and enables fast adaptation to component changes. The adaptivity results from a closed control loop of sensing and actuation. Visual and force-based systems, as well as combinations of both, are used as sensor systems. An assembly operation may proceed as follows: first, the target position is localised using a camera system and an approach position is reached. Subsequently, the component can be placed at the assembly position under force control using force-torque sensing.

Description

The aim of this thesis is to develop an approach for the robotic assembly of sockets or connectors and to subsequently compare it with existing approaches. Visual and force-based methods may be considered individually or in combination.

The resulting robotic assembly approach can first be tested in a peg-in-hole setup, a frequently used test scenario, and subsequently evaluated in more complex real-world scenarios.

It is also possible to build on the findings of the “AI for Industry Challenge” competition organised by Intrinsic. Its goal was the development of an approach for the adaptive assembly of SFP and SC connectors.

For practical validation of the findings, a robot cell with a collaborative robot from Universal Robots at the Technology Transfer Centre Nürnberger Land in Lauf is available.

Requirements

Programming skills (preferably Python and/or C++)
Ideally initial experience with ROS
Interest in robotic assembly tasks

This topic is suitable for a bachelor’s, project or master’s thesis and can also be combined with further topics for an M-APR.

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, Martin-Luther-Straße 18, Lauf an der Pegnitz

Further information: TTZ Nürnberger Land · Mobile Robotics Lab

Grasping Unknown Components for Robotic Assembly

Mon, 07 Sep 2026 00:00:00 +0000

Motivation

Frequent component revisions pose a challenge for industrial assembly processes. With every change cycle, the design, colour and material of a component may change. In robotic assembly, this challenge creates a demand for robots capable of adaptation.

Topic Area

For assembly, the robot system has access to a stored assembly plan. This plan contains the component designations, the assembly sequence and the region, the latter serving to roughly localise the assembly position. In order to assemble components under these boundary conditions, the components at hand must be detected, matched to a component designation and their grasp pose determined. To date, neural networks are trained on already known objects. They are therefore limited with regard to capturing new, unknown components.

Description

The aim of this thesis is to overcome the limitation to previously known objects. An approach for detecting unknown objects and computing their grasp poses is to be developed and subsequently compared with existing approaches.

Adaptability can be achieved by combining visual sensing with methods of artificial intelligence. RGB and depth cameras form the interface between the robotic system and its environment. The resulting image data can be analysed using methods of artificial intelligence, e.g. neural networks, in order to detect and then grasp unknown components.

For practical validation of the findings, a robot cell with a collaborative robot from Universal Robots at the Technology Transfer Centre Nürnberger Land in Lauf is available.

Requirements

Programming skills (preferably Python and/or C++)
Ideally initial experience with ROS and neural networks
Interest in robotics and deep learning

This topic is suitable for a bachelor’s, project or master’s thesis and can also be combined with further topics for an M-APR.

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, Martin-Luther-Straße 18, Lauf an der Pegnitz

Further information: TTZ Nürnberger Land · Mobile Robotics Lab

Robotic Assembly | Mobile Robotics Research Group — Prof. Dr. Christian Pfitzner

Adaptive Component Handling for Intelligent Assembly

Motivation

Topic Area

Description

Requirements

Supervision

Grasping Unknown Components for Robotic Assembly

Motivation

Topic Area

Description

Requirements

Supervision