Papers
41
Total Citations
1,222
H-Index
18
About
Matteo Fumagalli is a pioneering robotics researcher whose work sits at the intersection of aerial robotics, manipulation systems, and human-robot interaction. He is best known for his foundational contributions to the field of aerial manipulation — the integration of robotic manipulators with unmanned aerial vehicles (UAVs) to enable physical interaction with the environment in otherwise inaccessible locations. Fumagalli's most influential work includes the design, modeling, and control of aerial manipulator prototypes using quadrotor platforms, earning over 160 citations, and a landmark study on flying robots for contact-based industrial inspection, cited over 140 times. His research on compliant aerial manipulators, which introduced passive and active joint designs to handle mid-air impacts, has further shaped a new generation of aerial robotic workers. Beyond manipulation, his contributions span variable stiffness actuation, vision-based obstacle avoidance for UAVs, perching mechanisms, and force feedback using tactile sensing. With a portfolio collectively accumulating nearly 900 citations, Fumagalli's work has had tangible impact on advancing autonomous aerial systems toward real-world industrial applications, including surface inspection, cleaning, and structural maintenance — tasks that demand both precision and physical robustness in the air.
Research Focus
Key Achievements
Top Papers
- 1
- 2Modeling and control of a flying robot for contact inspection140 citations · 2012
- 3Compliant Aerial Manipulators: Toward a New Generation of Aerial Robotic Workers132 citations · 2016
- 4Force feedback exploiting tactile and proximal force/torque sensing85 citations · 2012
- 5Mechanical design of a manipulation system for unmanned aerial vehicles79 citations · 2012
- 6
- 7Mechanism for perching on smooth surfaces using aerial impacts57 citations · 2016
- 8
- 9Robot Vision: Obstacle-Avoidance Techniques for Unmanned Aerial Vehicles43 citations · 2013
- 10The iCub Platform: A Tool for Studying Intrinsically Motivated Learning40 citations · 2012