In order to strengthen our dedicated team in the Robotics Innovation Center (RIC) research department in Bremen we are looking for a
(full-time/part-time, 6 – 9 months)
The Robotics Innovation Center research department, headed by Prof. Dr. Dr. h.c. Kirchner, develop robot systems that are used for complex tasks on land, under water, in the air, and in space. The recently established underactuated lab at DFKI-RIC is looking for outstanding students to join us in pushing the boundaries of highly dynamic and agile robots.
Highly dynamic robots require robustness guarantees for their controllers to ensure operations at the edge of the performance envelope. Region of Attraction analysis can provide robustness estimation for the controllers such as TVLQR, MPC etc. [1-3]. This allows the robots to be controlled while they are the edge of their performance capabilities. In this thesis, we aim to develop Sums-of-Squares [1, 4] based methods for region of attraction estimation for canonical underactuated systems such as torque-limited simple pendulum, Acrobot / Pendubot, Hopping leg, Quadruped, etc. The developed methods will be validated using the systems available at the underactuated lab.
Mathematical: Robot kinematics and dynamics, linear and non-linear control theory, optimization methods.
Programming: C/C++, Python, experience with writing controllers for real systems (such as LQR).
1. Majumdar, A., & Tedrake, R. (2017). Funnel libraries for real-time robust feedback motion planning. International Journal of Robotics Research, 36(8), 947–982. https://doi.org/10.1177/0278364917712421
2. Moore, J., Cory, R., & Tedrake, R. (2014). Robust post-stall perching with a simple fixed-wing glider using LQR-Trees. Bioinspiration and Biomimetics, 9(2). https://doi.org/10.1088/1748-3182/9/2/025013
3. Cory, R. E. (2010). Supermaneuverable perching (Massachusetts Institute of Technology). Retrieved from http://hdl.handle.net/1721.1/60142
4. Tedrake, R., Manchester, I. R., Tobenkin, M., & Roberts, J. W. (2010). LQR-trees: Feedback motion planning via sums-of-squares verification. International Journal of Robotics Research, 29(8), 1038–1052. https://doi.org/10.1177/0278364910369189
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