Papers
10
Total Citations
105
H-Index
4
About
Elisa Capello is a dynamic researcher whose work spans autonomous systems, robotics, and advanced control theory, with particular expertise in unmanned aerial and ground vehicles, precision agriculture, and space robotics. Her research consistently bridges theoretical innovation with real-world application, developing sophisticated guidance, navigation, and control (GNC) frameworks for complex and GPS-denied environments. Among her most influential contributions is her 2018 work on distributed architectures for UAV indoor navigation (43 citations), which addressed critical challenges in small UAV autonomy without reliance on GPS infrastructure. Her 2024 paper on Transformer-Based Model Predictive Control (35 citations) demonstrates her forward-thinking integration of machine learning with classical control theory, applying sequence modeling to solve notoriously difficult trajectory optimization problems. This breadth is further reflected in her work on sliding mode control for agricultural robots, bio-inspired path planners, Gaussian process-based system identification, and formation flying algorithms for space-based robotic manipulators. Capello's research is particularly impactful in the emerging field of Agriculture 4.0, where she explores reinforcement learning and adaptive control strategies for autonomous farming systems. With contributions spanning embedded hardware implementation, data-driven modeling, and novel locomotion platforms, she represents a versatile and prolific voice in modern robotics and autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1A novel distributed architecture for UAV indoor navigation43 citations · 2018
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