Silvia Rueda

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About

Silvia Rueda is a researcher whose work bridges robotics, virtual reality, and real-time simulation. Her primary research areas include collision detection algorithms, robotic motion platforms, and the integration of neural networks for interactive systems. Rueda’s most notable contribution is her pioneering work on a neural network approach for real-time collision detection, published in 2003. This algorithm, designed for box-shaped objects, leverages multilayer perceptrons to predict impact points based on object positions, enabling faster and more efficient collision handling in dynamic environments. With 14 citations, this paper remains a foundational reference for researchers developing real-time interactive applications, from gaming to industrial robotics. Rueda also advanced the field of virtual reality through her 2017 study on simulating robotic motion platforms using digital filters. This work addresses the challenging calibration of washout algorithms, which are critical for generating realistic self-motion in vehicle simulators and VR systems. By proposing a simulation-based approach, she reduced the need for extensive physical testing, making high-fidelity motion platforms more accessible. Rueda’s research demonstrates a consistent focus on improving computational efficiency and realism in human-machine interaction, earning her recognition among peers working at the intersection of robotics and virtual environments.