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About

Eva Dyllong is a researcher whose work lies at the intersection of robotics, computational geometry, and numerical verification. Her primary research areas include trajectory planning for robot manipulators, distance computation between complex geometric objects, and the application of spline techniques to enable real-time motion modifications. Dyllong’s major contributions are centered on developing accurate and verified algorithms for fundamental geometric problems in robotics. She pioneered methods for calculating the Euclidean distance between a point and NURBS surfaces, a critical task for collision detection and path planning, and advanced the use of the Gilbert-Johnson-Keerthi (GJK) distance algorithm for incremental motions, providing interval-based verification for enhanced reliability. Her work on distance computation between octree-encoded objects and convex hulls using interval optimization has furthered the field of verified computing, ensuring provably correct results in applications ranging from biomechanics to medical robotics. With her most-cited paper, "Planning and real-time modifications of a trajectory using spline techniques," garnering 39 citations, Dyllong’s research has provided foundational tools for safe and efficient robot navigation in complex environments, demonstrating a sustained impact on both theoretical and applied robotics.