Ph.D. Student at the University of Luebeck
Short bio: With January 2018, Nils Rottmann is a PhD student and research scientist at the Institute for Robotics and Cognitive Systems at the University of Luebeck. In his doctoral study, with the title “Smart Sensor, Navigation and Learning Strategies for low-cost lawn care Systems”, he develops low-cost sensor systems and investigates probabilistic learning and modeling approaches. His research addresses the challenges of learning adaptive control strategies from few and sparse data and to predict and plan complex motions in dynamical systems.
He holds a master’s degree in Theoretical Mechanical Engineering from the Hamburg University of Technology, Germany. Nils Rottmann graduated with honors in December 2017 with a thesis entitled „Geometric Control and Stochastic Trajectory Planning for Underwater Robotic Systems“.
Research Interests
- Robotics: Mobile Robotics, Sensor Development, Robot-Operating-System (ROS), Mobile Navigation, Path Planning, Complete Coverage Path Planning, Probabilistic Robotics.
- Machine Learning: Non-Linear Regression, Graphical Models, Probabilistic Inference, Variational Inference, Gaussian Processes, Bayesian Optimization.
Contact & Quick Links
M.Sc. Nils Rottmann
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since March 2018.
Ratzeburger Allee 160,
23562 Lübeck,
Deutschland
Phone: +49 451 3101 – 5222
Email: rottmann@rob.uni-luebeck.de
Web: https://nrottmann.github.io
CV of M.Sc. Nils Rottmann
DBLP
Frontiers Network
GitHub
Google Citations
LinkedIn
ORCID
Research Gate
Publcations
2021 |
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Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Proceedings Article In: European Conference on Mobile Robots (ECMR 2021), 2021. |  |
Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article In: Advanced Intelligent Systems, pp. 1–28, 2021. |  |
2020 |
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Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Proceedings Article In: IEEE SENSORS , pp. 1–4, 2020. |  |
Learning Hierarchical Acquisition Functions for Bayesian Optimization Proceedings Article In: International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020. |  |
Parameter Optimization for Loop Closure Detection in Closed Environments Proceedings Article In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020. |  |
A novel Chlorophyll Fluorescence based approach for Mowing Area Classification Journal Article In: IEEE Sensors Journal, 2020. |  |
Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Proceedings Article In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. |  |
2019 |
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Loop Closure Detection in Closed Environments Proceedings Article In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9. |  |
Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Proceedings Article In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019). |  |