Papers
83
Total Citations
1,549
H-Index
22
About
Sauro Longhi is a prominent robotics and control systems researcher whose work has fundamentally advanced the fields of mobile robot localization, intelligent control, and autonomous systems. His groundbreaking 1999 study on adaptive extended Kalman filtering for mobile robot localization — now with over 319 citations — established foundational techniques for fusing odometric and sonar sensor data, becoming a cornerstone reference in autonomous navigation research. Longhi's contributions span a remarkably broad spectrum: from fuzzy logic-enhanced Kalman filters and neural network-based controllers to discrete-time sliding mode control of robotic manipulators, where his 2012 work integrating radial basis function neural networks demonstrated elegant solutions for handling real-world system uncertainties. His humanitarian instincts are equally evident, with notable research developing navigation systems for powered wheelchairs to enhance independence for individuals with motor disabilities. More recently, Longhi has embraced Industry 4.0 challenges, contributing to predictive maintenance methodologies and publishing influential survey work on ROS2 frameworks for autonomous robots. His research on UGV-UAV cooperative missions further highlights his vision for heterogeneous multi-robot systems. Across more than two decades, Longhi's consistently cited body of work reflects a career dedicated to bridging theoretical rigor with real-world experimental validation.
Research Focus
Key Achievements
Top Papers
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- 8Learning control of mobile robots using a multiprocessor system43 citations · 2005
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