Fabio Marchese

About

Fabio Marchese is an Italian robotics researcher whose work spans autonomous robot navigation, omnidirectional vision systems, and multi-robot coordination. His most influential contributions lie at the intersection of computer vision and motion planning, where he has consistently developed practical, computationally efficient solutions for real-world robotic systems. Marchese gained early recognition for his pioneering work on catadioptric omnidirectional vision, including innovative multi-part mirror designs, which earned him over 90 combined citations and proved particularly impactful in the context of soccer robotics — a demanding testbed for autonomous systems. Equally notable is his sustained development of Cellular Automata (CA)-based path-planning algorithms, an elegant approach that leverages the parallel, grid-based nature of cellular automata to compute shortest collision-free paths efficiently, even for robots with generic shapes, variable kinematics, and complex terrains. His work on multi-robot systems, including the MCA framework for coordinating teams of mobile robots, reflects a natural evolution toward increasingly complex, real-world deployable architectures. Complementing these contributions is his MUREA localization method, which uses multi-resolution evidence accumulation to enable robust robot self-localization in known environments. With over 210 cumulative citations, Marchese's body of work represents a coherent and lasting contribution to foundational mobile robotics research.

Research Focus

Key Achievements

Top Papers

1. 1
   Omni-directional catadioptric vision for soccer robots

   62 citations · 2001
2. 2
   A directional diffusion algorithm on cellular automata for robot path-planning

   31 citations · 2002
3. 3
   Omni-directional vision with a multi-part mirror

   31 citations · 2001
4. 4
   Multiple Mobile Robots Path-Planning with MCA

   26 citations · 2006
5. 5
   Cellular automata in robot path planning

   20 citations · 2002
6. 6
   A robot localization method based on evidence accumulation and multi-resolution

   12 citations · 2003
7. 7
   Path-Planning for Multiple Generic-Shaped Mobile Robots with MCA

   10 citations · 2006
8. 8
   MUREA: A MUlti-Resolution Evidence Accumulation Method for Robot Localization in Known Environments

   10 citations · 2003
9. 9
   A reactive planner for mobile robots with generic shapes and kinematics on variable terrains

   7 citations · 2006
10. 10
    MRS Motion Planning: the Spatiotemporal MultiLayered Cellular Automata Approach

    5 citations · 2011

Key Collaborators

Contact & Links