Alessandro Rucco
Papers
2
Total Citations
10
H-Index
2
About
Alessandro Rucco is a researcher specializing in autonomous robotics, trajectory optimization, and cooperative control of multi-vehicle systems. His work focuses on developing algorithmic frameworks that enable constrained robotic vehicles—such as drones, underwater gliders, or ground rovers—to execute complex maneuvers with precision and efficiency. Rucco’s major contributions include the introduction of a virtual target approach for trajectory optimization, which allows a general class of constrained vehicles to compute locally optimal feasible paths that best approximate desired maneuvers in the least-squares sense. This method bridges the gap between geometric tracking and trajectory planning, offering a powerful tool for real-time applications. He has also advanced the field of multi-robot coordination through a sampled-data model predictive framework for cooperative path following, enabling multiple vehicles to maintain formation while adhering to communication and dynamic constraints. Although his most-cited works—such as the 2015 paper on virtual target optimization (7 citations) and the 2017 cooperative control study (3 citations)—are early in their citation lifecycle, they represent foundational steps toward scalable, robust autonomy. Rucco’s research is particularly relevant for students and engineers working on autonomous navigation, swarm robotics, and constrained motion planning.
Research Focus
Key Achievements
Top Papers
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