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About

D. Carnevale is a robotics researcher whose work centers on distributed control architectures, learning control algorithms, and sensor fusion for autonomous systems. A key contribution is the development of a novel distributed architecture for unmanned aircraft systems (UAS) based on Robot Operating System 2 (ROS 2), which leverages industrial-grade tools to enhance reliability in high-stakes environments—a paper that has garnered 14 citations since 2023. Carnevale also advanced repetitive learning control for robotic manipulators, achieving asymptotic joint position tracking through a recursive period identifier, validated experimentally in 2018 (6 citations). In the realm of autonomous agents, Carnevale addressed efficient visual sensor fusion, combining multiple vision sensors and visual odometry sources to improve localization in unknown environments (2023, 3 citations). Earlier work on state estimation for robots with complementary redundant sensors (2015, 3 citations) tackled the challenge of fusing sharp but ambiguous measurements with unambiguous data, laying groundwork for robust pose estimation. These contributions reflect a focus on practical, real-world deployment of autonomous systems, from aerial vehicles to manipulators, with an emphasis on reliability and efficiency.