Computational Intelligence in Robotics (CIR)

The junior research group Computational Intelligence in Robotics (CIR) deals with the use of computational intelligence methods such as: Evolutionary Algorithms, Swarm Intelligence and Deep Learning to solve current problems in the design and robustness of robotic systems. The focus is on estimating the system state of robot swarms including the current uncertainties and generating appropriate behaviors to react to them. The junior research group currently has two specific funded projects in the field of autonomous driving and autonomous agriculture.  Additionally, the group works on practical robotic projects aiming to transfer theoretic results in to practical applications for swarms of quadcopters and within the RoboCop international competition.

 Head of the Young Research Scientist Group

Dr.-Ing. Christoph Steup (steup at

Team Members: 




  • Robot Operating Systems (ROS) - Usage and Development
  • Localization of Mobile Platforms
  • Uncertainty-aware State Estimation
  • Uncertainty-aware AI Methods
  • Uncertainty-aware Sensor Fusion
  • State-Estimation in Unstructured Environments
  • Complex Multi-Objective Route-Planning
  • Autonomous Driving
  • Agricultural Robotics


  • Eva Röper, Jens Weise, Christoph Steup and Sanaz Mostaghim
  • Innovization for Route Planning Applied to an Uber Movement Speeds Dataset for Berlin
  • Accepted at PPSN Conference, 2024


  • Dominik Weikert, Christoph Steup, Sanaz Mostaghim
  • Adverse Weather Benchmark Dataset for LiDAR-based 3D Object Recognition and Segmentation in Autonomous Driving
  • Accepted at IEEE Conference on AI, Singapore, June 2024


  • Lukas Bostelmann-Arp, Christoph Steup and Sanaz Mostaghim
  • Free-Form Coverage Path Planning of Quadcopter Swarms for Search and Rescue Missions using Multi-Objective Optimization
  • Accepted at IEEE World Congress on Computational Intelligence, Yokohama, Japan, 2024


  • Adrian Köring and Christoph Steup
  • PyramidEnsemble: Joining Large and Small Models
  • 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 689-694, doi: 10.1109/SSCI52147.2023.10371902.


  • Lukas Bostelmann-Arp, Christoph Steup and Sanaz Mostaghim
  • Linking Field Decomposition and Coverage Path Planning: A Coevolution Approach
  • 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, 2023, pp. 294-295, doi:


  • Lukas Bostelmann-Arp, Christoph Steup and Sanaz Mostaghim
  • Multi-Objective Seed Curve Optimization for Coverage Path Planning in Precision Farming
  • In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23). Association for Computing Machinery, New York, NY, USA, 1312–1320.

Last Modification: 10.07.2024 - Contact Person: Webmaster