Tiago Giacomelli Alves
Papers
2
Total Citations
13
H-Index
2
About
Tiago Giacomelli Alves is a robotics and control systems researcher whose work focuses on mobile robot dynamics, system identification, and advanced controller design. His research centers on differential-drive mobile robots — one of the most widely used platforms in autonomous robotics — where he has made meaningful contributions to both modeling and control methodologies. In his most-cited work (2018, 11 citations), Alves developed a dynamic model for differential-drive robots that elegantly incorporates actuator effects while enabling parameter identification through a linearized formulation compatible with the recursive least squares algorithm. This practical approach bridges theoretical modeling with real-world implementation, making it particularly valuable for robotics practitioners and researchers alike. Complementing this, his work on non-linear pose stabilization introduces partial feedback linearization techniques that simplify complex robot dynamics, enabling the application of linear control laws alongside cascading controller architectures. His optimization-based tuning methodology further enhances the practical deployment of these controllers. Though early in citation accumulation, Alves' contributions address fundamental challenges in autonomous robot navigation and control — areas critical to fields ranging from warehouse automation to service robotics. His research is especially relevant for engineers and students seeking rigorous yet implementable solutions to mobile robot control problems.
Research Focus
Key Achievements
Top Papers
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