Papers
11
Total Citations
157
H-Index
7
About
M. De Cecco is a leading researcher in autonomous mobile robotics, with a focus on sensor fusion, navigation, and human-assistive technologies. His work spans industrial automation and assistive robotics, particularly for autonomous guided vehicles (AGVs) and wheelchairs. A major contribution is the development of the Robust Localization and Pallet Finding (RLPF) algorithm, which combines laser and camera data for autonomous pallet picking in industrial forklifts—a key advancement for logistics automation (42 citations). He also pioneered sensor fusion algorithms for inertial-odometric navigation, enabling AGVs to self-calibrate using absolute-reference measurements, significantly improving accuracy in dynamic environments (26 and 18 citations). More recently, De Cecco has advanced assistive robotics, including the RoboEYE semi-autonomous gaze-driven wheelchair for users with severe motor disabilities (10 citations) and automatic calibration of multi-camera motion capture systems using graph-based spatiotemporal methods (23 citations). His work on object localization with laser rangefinders and path following controllers for rhombic-like vehicles further demonstrates his versatility. With over 150 total citations, De Cecco’s research bridges robust industrial automation and accessible assistive technologies, making tangible impacts on both factory floors and domestic environments.
Research Focus
Key Achievements
Top Papers
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- 8Four path following controllers for rhombic like vehicles6 citations · 2013
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- 10Reactive Simulation for Real-Time Obstacle Avoidance2 citations · 2008