Bachelor’s/Master’s thesis on data-driven energy modeling of industrial robots for energy-efficient trajectory optimization

Data-driven energy modeling of industrial robots for energy-efficient trajectory optimization
ContactName:  Dr.-Ing. Christoph Henke (IfU) Email: christoph.henke@ifu.rwth-aachen.de Type of Thesis: Bachelor & Master

The energy-efficient design of industrial production processes is becoming increasingly important in light of rising energy costs and sustainability requirements. Industrial robotics in particular offers great potential for reducing energy consumption through targeted modeling and optimization of motion sequences.

As part of a research project, data-driven energy modeling and optimization of industrial robots is being developed, which should enable cross-industry application through connection to existing data ecosystems. The focus is on robot systems in ongoing production lines, as typically used in the automotive industry.

The focus is on developing data-driven methods for manufacturer-independent energy modeling of industrial robots. Parameter exploration and system identification methods will be used to create a predictive model for the energy consumption of robot movements. This model forms the basis for optimization approaches that can be used to plan energy-efficient robot trajectories while complying with application-specific requirements (e.g., cycle times, path restrictions).

Possible research questions/topics:

• Which parameters (e.g., torque, speed, position, trajectory shape) are particularly relevant for energy consumption modeling, and how can they be efficiently identified?
• How can analytical and data-driven modeling approaches be combined to improve model quality (e.g., using Bayesian optimization)?
• How can the developed energy model be validated experimentally on real robot test benches?

Prerequisites:

• Degree in robotics, mechatronics, mechanical engineering, CES, computer science, data science, or related fields
• Interest in energy efficiency, robotics, and data-driven modeling
• Basic knowledge of C++, Python, or a comparable programming language
• Experience with data analysis, system identification, or machine learning is an advantage
  Interest in experimental work on industrial robot test benches

We offer:

• Collaboration in a current, application-oriented research project with industrial relevance
• Experimental validation on real robot test benches
• Opportunity to combine theoretical modeling and practical application