Karen L. Poblete
Papers
3
Total Citations
60
H-Index
3
About
Karen L. Poblete is a robotics researcher whose work focuses on advancing motion planning and collision detection for complex-shaped robots. Her key contributions lie in developing efficient algorithms for robots with ellipsoidal components, addressing fundamental challenges in path planning and geometric computation. Poblete’s most cited paper, “Efficient Path Planning in Narrow Passages for Robots With Ellipsoidal Components” (2022, 48 citations), tackles the computational bottlenecks of sampling-based planners like PRM and RRT in constrained environments. She further innovated with “Efficient Exact Collision Detection between Ellipsoids and Superquadrics via Closed-form Minkowski Sums” (2019, 7 citations), providing a closed-form solution that eliminates iterative approximations, and “Path Planning for Ellipsoidal Robots and General Obstacles via Closed-Form Characterization of Minkowski Operations” (2020, 5 citations). Her work is notable for introducing mathematically elegant, exact methods that improve both speed and accuracy, with potential applications in computer graphics, CAD, and autonomous systems. Poblete’s research bridges theoretical geometry and practical robotics, offering tools that make motion planning more reliable for non-spherical robots.
Research Focus
Key Achievements
Top Papers
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