Goele Pipeleers
Papers
31
Total Citations
567
H-Index
11
About
Goele Pipeleers is a researcher whose work sits at the intersection of robotics, optimal control, and motion planning. Her research focuses on developing computationally efficient methods for trajectory optimization, path following, and model predictive control (MPC) for robotic systems, with particular emphasis on making these techniques viable for real-time deployment. Among her most influential contributions is her work on time-optimal path following for robot manipulators, where she extended convex reformulations to handle more complex, real-world constraints such as jerk limitations and Cartesian forces—papers that have collectively accumulated nearly 250 citations. Her 2017 work on distributed MPC for multi-vehicle formation control (99 citations) demonstrates her breadth across robotics domains, while her 2018 paper on embedded NMPC for obstacle avoidance using the PANOC solver (98 citations) highlights her commitment to practical, embedded implementations. Pipeleers has also made notable advances in B-spline trajectory parameterization and differentially flat system formulations, enabling guaranteed constraint satisfaction in motion planning. Across her body of work, she consistently bridges theoretical rigor with engineering applicability, making her research particularly valuable to roboticists seeking deployable, high-performance motion control solutions.
Research Focus
Key Achievements
Top Papers
- 1Distributed MPC for multi-vehicle systems moving in formation99 citations · 2017
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