Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems
2012291 citationsCarlo Pinciroli, Vito Trianni et al.Swarm Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Carlo Pinciroli
Since
Specialization
Citations
This map shows the geographic impact of Carlo Pinciroli'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 Carlo Pinciroli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlo Pinciroli more than expected).
This network shows the impact of papers produced by Carlo Pinciroli. 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 Carlo Pinciroli. The network helps show where Carlo Pinciroli may publish in the future.
Co-authorship network of co-authors of Carlo Pinciroli
This figure shows the co-authorship network connecting the top 25 collaborators of Carlo Pinciroli.
A scholar is included among the top collaborators of Carlo Pinciroli 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 Carlo Pinciroli. Carlo Pinciroli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pinciroli, Carlo, et al.. (2015). A Tuple Space for Data Sharing in Robot Swarms.. 287–294.10 indexed citations
10.
Pinciroli, Carlo, et al.. (2014). SRoCS: Leveraging stigmergy on a multi-robot construction platform for unknown environments. Lecture notes in computer science. 8667. 158–169.2 indexed citations
11.
Brambilla, Manuele, Carlo Pinciroli, Mauro Birattari, & Marco Dorigo. (2012). Property-driven design for swarm robotics. Adaptive Agents and Multi-Agents Systems. 139–146.23 indexed citations
Roli, Andrea, et al.. (2011). Robustness, evolvability and complexity in Boolean network robots. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna).2 indexed citations
14.
Reina, Andreagiovanni, Carlo Pinciroli, Eliseo Ferrante, et al.. (2011). Closed-Loop Aerial Robot-Assisted Navigation of a Cohesive Ground-Based Robot Swarm.1 indexed citations
Brambilla, Manuele, Carlo Pinciroli, Mauro Birattari, & Marco Dorigo. (2009). A reliable distributed algorithm for group size estimation with minimal communication requirements. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 1–6.5 indexed citations
18.
Peña, Jorge, et al.. (2009). Heterogeneous Particle Swarm Optimizers. IEEE Transactions on Evolutionary Computation. 698–705.17 indexed citations
19.
Pinciroli, Carlo, Rehan O’Grady, Anders Lyhne Christensen, & Marco Dorigo. (2009). Self-organised recruitment in a heteregeneous swarm. 1–8.8 indexed citations
20.
O’Grady, Rehan, Carlo Pinciroli, Anders Lyhne Christensen, & Marco Dorigo. (2009). Supervised Group Size Regulation in a Heterogeneous Robotic Swarm. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 113–119.8 indexed citations
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.