Francesco Amigoni

130 papers receiving 1.6k citations

Peers

Francesco Amigoni
Comparison fields: 5 of 106
  • Computer Vision and Pattern Recognition 821
  • Aerospace Engineering 674
  • Computer Networks and Communications 641
  • Artificial Intelligence 403
  • Mechanical Engineering 264
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Patrick Doherty Sweden
Esther M. Arkin United States
Shaoshan Liu United States
Omar Y. Al-Jarrah United Kingdom
Jie Tang China
Shubhani Aggarwal India
Hongzi Zhu China
Edward Tunstel United States
Insaf Ullah Pakistan
Chung‐Nan Lee Taiwan
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Citations per field
00.5×3.2×
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Citations per year

Countries citing papers authored by Francesco Amigoni

Since Specialization
Citations

This map shows the geographic impact of Francesco Amigoni's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesco Amigoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Amigoni more than expected).

Fields of papers citing papers by Francesco Amigoni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Francesco Amigoni. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesco Amigoni. The network helps show where Francesco Amigoni may publish in the future.

Co-authorship network of co-authors of Francesco Amigoni

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Amigoni. A scholar is included among the top collaborators of Francesco Amigoni based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesco Amigoni. Francesco Amigoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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11
Communication-Constrained Multirobot Exploration: Short Taxonomy and Comparative Results
3
12
Standard for Robot Map Data Representation for Navigation
4
13 6
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Distributed Agents for Autonomy
2
15
Evaluating the Efficiency of Frontier-based Exploration Strategies
84
16
Developing a Deterministic Patrolling Strategy for Security Agents
6
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Exploration Strategies based on Multi-Criteria Decision Making for an Autonomous Mobile Robot
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19
Multiagent systems for cardiac pacing simulation and control
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An Environmental Multiagent Architecture for Health Management
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About Francesco Amigoni

Francesco Amigoni is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Aerospace Engineering, having authored 139 papers that have together received 1.7k indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (43 papers), Robotics and Sensor-Based Localization (38 papers) and Optimization and Search Problems (34 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (821 citations), Computer Networks and Communications (641 citations) and Aerospace Engineering (674 citations). Francesco Amigoni has collaborated with scholars based in Italy, United States and Germany. Frequent co-authors include Nicola Basilico, Nicola Gatti, Jacopo Banfi, Vincenzo Caglioti, Matteo Luperto, Alberto Quattrini Li, Viola Schiaffonati, Sven Behnke, Dirk Holz and Simone Gasparini. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Proceedings of the IEEE and Water Resources Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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