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Directional Statistics

Robotic Beating Heart Surgery

Stochastic Control

Fig. 1: Illustration of chance constraints.

Unlike classical control, stochastic control takes uncertainties into account and models them as random variables that are distributed according to certain probability distributions. This involves a number of additional challenges.

In particular, I have worked on the following problems.

  • Model-predictive control on the unit circle [1]
  • Chance-constrained control [2], [3]
  • Control of Markov Jump Linear Systems (MJLS) [4]
  • Nonlinear optimal control [5]

Other Projects

  • Stochastic Hybrid Systems [6]
  • Mobile Robotics [7]
  • Nonlinear Filtering [8] [9]
  • Optical Belt Sorting [10]
  • SLAM [11]

1. a Gerhard Kurz, Maxim Dolgov, Uwe D. Hanebeck, 2015. Nonlinear Stochastic Model Predictive Control in the Circular Domain. Proceedings of the 2015 American Control Conference (ACC 2015), Chicago, Illinois, USA.
2. a Maxim Dolgov, Gerhard Kurz, Uwe D. Hanebeck, 2015. Chance-constrained Model Predictive Control based on Box Approximations. Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015), Osaka, Japan.
3. a Gerhard Kurz, Maxim Dolgov, Uwe D. Hanebeck, 2016. Progressive Closed-Loop Chance-Constrained Control. Proceedings of the 19th International Conference on Information Fusion (Fusion 2016), Heidelberg, Germany.
4. a Maxim Dolgov, Gerhard Kurz, Uwe D. Hanebeck, 2016. Finite-horizon Dynamic Compensation of Markov Jump Linear Systems without Mode Observation. Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, Nevada, USA.
5. a Maxim Dolgov, Gerhard Kurz, Daniela Grimm, Florian Rosenthal, Uwe D. Hanebeck, 2018. Stochastic Optimal Control Using Local Sample-Based Value Function Approximation. Proceedings of the 2018 American Control Conference (ACC 2018), Milwaukee, Wisconsin, USA.
6. a Maxim Dolgov, Gerhard Kurz, Uwe D. Hanebeck, 2014. State Estimation for Stochastic Hybrid Systems Based on Deterministic Dirac Mixture Approximation. Proceedings of the 2014 American Control Conference (ACC 2014), Portland, Oregon, USA.
7. a Jan Oberländer, Tanja Harbaum, Gerhard Kurz, Nadia Ahmed, Tomislav Kos-Grabar, Andreas Hermann, Arne Rönnau, Rüdiger Dillmann, 2011. A Student-built Ball-throwing Robotic Companion for Hands-on Robotics Education. Proceedings of the 14th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR), Paris, France, pp.233–240.
8. a Gerhard Kurz, Uwe D. Hanebeck, 2017. Linear Regression Kalman Filtering Based on Hyperspherical Deterministic Sampling. Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), Melbourne, Australia.
9. a Gerhard Kurz, Uwe D. Hanebeck, 2018. Improved Progressive Gaussian Filtering Using LRKF Priors. Proceedings of the 2018 American Control Conference (ACC 2018), Milwaukee, Wisconsin, USA.
10. a Florian Pfaff, Gerhard Kurz, Christoph Pieper, Georg Maier, Benjamin Noack, Harald Kruggel-Emden, Robin Gruna, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Laengle, Juergen Beyerer, 2017. Improving Multitarget Tracking Using Orientation Estimates for Sorting Bulk Materials. Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, Korea.
11. a Kailai Li, Gerhard Kurz, Lukas Bernreiter, Uwe D. Hanebeck, 2018. Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter. Proceedings of the 21st International Conference on Information Fusion (Fusion 2018), Cambridge, United Kingdom.

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