Papers
99
Total Citations
1,943
H-Index
23
About
Francesco Amigoni is a prominent robotics researcher whose work centers on autonomous mobile robot exploration, multi-robot coordination, and experimental methodology in robotics. Based at Politecnico di Milano, he has made foundational contributions to how robots navigate and map unknown environments, a challenge critical to search-and-rescue operations, disaster response, and autonomous navigation. Amigoni's most influential work focuses on frontier-based exploration strategies—algorithms that guide robots to efficiently discover unknown spaces. His evaluations and refinements of these strategies (2008–2010, totaling over 170 citations) established key benchmarks for the field. He pioneered information-theoretic and multi-criteria decision-making approaches to exploration, enabling robots to balance competing objectives more intelligently. His highly cited 2017 survey on multirobot exploration under communication constraints (123 citations) synthesized a decade of progress while identifying critical open challenges in coordinating robot teams with limited connectivity. Beyond exploration, Amigoni tackled the computational complexity of multi-robot path planning, proving intractability results for time-optimal coordination on grid graphs. He also contributed meaningfully to robotics research methodology, advocating for rigorous experimental standards and competition-based benchmarking. Collectively, his work has accumulated over 700 citations, profoundly shaping how autonomous robot teams are designed and evaluated.
Research Focus
Key Achievements
Top Papers
- 1Multirobot Exploration of Communication-Restricted Environments: A Survey123 citations · 2017
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- 3Evaluating the Efficiency of Frontier-based Exploration Strategies110 citations · 2010
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- 6Experimental evaluation of some exploration strategies for mobile robots63 citations · 2008
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- 10A Multi-Objective Exploration Strategy for Mobile Robots55 citations · 2006