Asier Zubizarreta
Papers
21
Total Citations
258
H-Index
9
About
Asier Zubizarreta is a robotics researcher whose work spans two deeply interconnected domains: the modeling and control of parallel robots, and the development of robotic systems for upper limb rehabilitation. His foundational contributions to parallel robotics include pioneering redundant dynamic modeling approaches for mechanisms such as the Stewart-Gough platform and the 3RRR parallel robot, demonstrating that incorporating extra sensor data significantly enhances control performance and precision — work that has collectively attracted over 90 citations. His 2017 paper applying neural networks to solve the real-time direct kinematic problem of the 3PRS robot stands as his most influential contribution, with 55 citations, reflecting the growing importance of data-driven methods in robotics. Zubizarreta subsequently extended his expertise toward rehabilitation engineering, contributing to the design of the Universal Haptic Pantograph, a multi-configurable upper limb rehabilitation device, alongside control frameworks and virtual sensor methodologies that make safe, cost-effective robot-mediated therapy more accessible. His 2019 work on inclusive and seamless control for rehabilitation robots underscores a commitment to patient-centered design. Spanning foundational mechanism theory to clinical applications, Zubizarreta's research offers students a compelling example of how rigorous robotics engineering translates into meaningful human benefit.
Research Focus
Key Achievements
Top Papers
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- 4Redundant sensor based control of the 3RRR parallel robot30 citations · 2012
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- 6Virtual Sensors for Advanced Controllers in Rehabilitation Robotics13 citations · 2018
- 7A redundant dynamic model of parallel robots for model-based control13 citations · 2012
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- 9Control of parallel robots using passive sensor data9 citations · 2008
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