Alessandro Riva
Papers
7
Total Citations
22
H-Index
3
About
Alessandro Riva’s research lies at the intersection of autonomous mobile robotics and algorithmic optimization, with a focus on enabling robots to navigate, map, and perform tasks in complex, real-world environments. His key contributions span semantic mapping, multi-robot coordination, and path planning under constraints. Notably, his work on semantic classification uses statistical relational learning to reason about the entire structure of buildings, allowing robots to label rooms (e.g., “corridor” or “office”) with greater accuracy than local-feature methods. Riva has also advanced the theory and practice of multi-robot coverage, formulating it as a multi-Traveling Salesperson Problem and developing a GRASP metaheuristic for efficient grid coverage—critical for applications like cleaning, patrolling, and precision agriculture. His research on limited-buffer shortest path problems addresses the practical challenge of robots collecting data (e.g., video feeds) with constrained onboard memory, while his work on joint measurement paths supports tasks like constructing communication maps. With over 20 citations across his most-cited papers, Riva’s contributions are steadily gaining recognition for their practical relevance and algorithmic rigor, making him a notable figure in the robotics and operations research communities.
Research Focus
Key Achievements
Top Papers
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- 6Some Performance Bounds of Strategies for Graph Exploration2 citations · 2015
- 7Multirobot Coverage of Modular Environments2 citations · 2020