Daniel Perea
Papers
2
Total Citations
27
H-Index
2
About
Daniel Perea is a robotics researcher whose work focuses on sensor fusion, localization, and the stability of unmanned aerial vehicles (UAVs). His key contributions lie in advancing Monte Carlo Localization (MCL) techniques by developing a novel weighting mechanism that fuses heterogeneous sensor data, enabling mobile robots to compensate for individual sensor flaws and achieve more robust pose estimation. This work, published in 2013, has garnered 16 citations and remains relevant for researchers tackling real-world localization challenges. Perea has also made significant contributions to UAV safety and design, analyzing the stability and performance trade-offs between Quadrotor and Hexrotor platforms. His 2015 study, cited 11 times, models the impact of rotor failure on these multirotor helicopters, demonstrating that Hexrotors offer superior robustness and fault tolerance—a critical insight for applications requiring high reliability. By combining theoretical analysis with practical uncertainty modeling, Perea’s research bridges the gap between sensor fusion algorithms and aerial vehicle dynamics, providing foundational knowledge for students and engineers working on autonomous navigation and resilient drone design.
Research Focus
Key Achievements
Top Papers
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- 2