Bin Picking of Cables

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

Motivation

Cables are relevant in a large number of electronic products. Handling and assembling cables is therefore an essential task for robots in the automated manufacturing of these products. Due to their limpness, cables pose a challenge for robotics. Because of this property, cables can become knotted, damage to the cable can occur, or difficulties can arise during assembly.

Topic Area

To date, cables are fed individually and in an ordered state into the robotic assembly process. This allows the robot to grasp and localise the connectors easily. Particularly for short cables, this requires pre-sorting. Feeding the cables in a bin involves considerably less effort. In order to separate the cables for assembly, the robot reaches into the bin, a process known as bin picking. However, numerous challenges arise during separation. Cables can become entangled by their connectors while being grasped, cables can become knotted, and connectors may not be identified unambiguously due to overlaps.

Description

The aim of this thesis is to develop an approach for separating cables and to subsequently compare it with other approaches. The approach should localise connectors and determine grasp poses. A concept for dealing with mutual occlusions of the cables is to be developed. A further goal is to track the connector pose during the handling operation in order to provide reliable coordinates for the subsequent assembly.

For practical validation of the developed concepts, collaborative robots from Universal Robots and, in the near future, a dual arm robot are available. The latter is a humanoid robot without legs. The robots are located at the Technology Transfer Centre Nürnberger Land in Lauf.

Requirements

Programming skills (preferably Python and/or C++)
Ideally initial experience with ROS
Interest in bin picking

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

Object Detection | Mobile Robotics Research Group — Prof. Dr. Christian Pfitzner

Bin Picking of Cables

Motivation

Topic Area

Description

Requirements

Supervision

Grasping Unknown Components for Robotic Assembly

Motivation

Topic Area

Description

Requirements

Supervision