Advancing Perception Systems for Robotic Safety and Automation

This master thesis investigates innovative perception systems for enhancing safety and efficiency in robotic automation. The study focuses on the development and implementation of a modular perception attachment capable of integrating multi-modal sensors into robotic manipulators. By enabling real-time 360-degree occupancy mapping and path planning, the research aims to address key challenges in manufacturing and automation, including unplanned downtime, safety compliance, and labor shortages. The thesis explores different aspects of the potential of these technologies to create a safe, cage-free working environment for collaborative robots in diverse industrial applications. The precise topic can be defined jointly with the supervisors in the project context based on the students expertise.