Thies Oelerich
Papers
2
Total Citations
8
H-Index
2
About
Thies Oelerich is an emerging robotics researcher whose work centers on advanced trajectory planning and motion control for robot manipulators. His research addresses one of the most pressing challenges in modern robotics: enabling robots to react intelligently and efficiently to dynamic, changing environments in real time. Oelerich's most notable contribution is the development of BoundMPC, a model predictive control strategy that allows robot manipulators to follow Cartesian reference paths — including complex via-points — while maintaining precise error bounds in both position and orientation. This online joint-space trajectory planner represents a significant step forward in making robot motion planning both flexible and computationally feasible. Complementing this, his BoundPlanner framework introduces a convex-set-based approach to bounded manipulator trajectory planning, tackling the critical bottleneck of computation speed that has historically limited online planning methods in challenging environments. With his 2025 publications already accumulating citations — BoundMPC earning 6 citations and BoundPlanner 2 — Oelerich is establishing a focused research identity at the intersection of optimal control and robotic motion planning. His contributions offer practical pathways toward more responsive, reliable robot systems, making his work particularly relevant for researchers and engineers working on autonomous manipulation and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2