Papers
13
Total Citations
175
H-Index
6
About
Daniel Gandolfo is an accomplished robotics researcher whose work spans autonomous aerial systems, cooperative robotics, and advanced control theory. His most significant contribution lies in the domain of quadrotor UAVs, where his 2016 paper on energy-efficient path-following control for quadrotor helicopters has garnered 83 citations, establishing him as a notable voice in the challenge of sustainable aerial robotics. Building on this foundation, Gandolfo has made meaningful advances in trajectory tracking using linear algebra approaches and Euler-Lagrange modeling, as well as pioneering cooperative UAV systems capable of transporting cable-suspended payloads while managing collision avoidance and wind disturbances. His research has progressively expanded into heterogeneous robot formations, aerial robotic manipulators, and data-driven nonlinear model predictive control (NMPC), reflecting a forward-looking integration of machine learning with classical control frameworks. More recently, Gandolfo has embraced Education 4.0, developing Digital Twin platforms that democratize access to robotic manipulator training. With over 160 cumulative citations across a diverse and growing body of work, he represents an innovative researcher bridging foundational aerial robotics with emerging intelligent systems, making his publications essential reading for students and engineers working at the intersection of UAVs, manipulation, and autonomous cooperative systems.
Research Focus
Key Achievements
Top Papers
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- 4Adaptive Neural Compensator for Robotic Systems Control10 citations · 2019
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- 7Open-Access Platform for the Simulation of Aerial Robotic Manipulators4 citations · 2024
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- 9Adaptive NMPC-RBF with Application to Manipulator Robots3 citations · 2023
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