Lorenzo Pollini

2.0k total citations
131 papers, 1.5k citations indexed

About

Lorenzo Pollini is a scholar working on Aerospace Engineering, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Lorenzo Pollini has authored 131 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Aerospace Engineering, 49 papers in Computer Vision and Pattern Recognition and 38 papers in Control and Systems Engineering. Recurrent topics in Lorenzo Pollini's work include Robotic Path Planning Algorithms (40 papers), Aerospace and Aviation Technology (28 papers) and Human-Automation Interaction and Safety (26 papers). Lorenzo Pollini is often cited by papers focused on Robotic Path Planning Algorithms (40 papers), Aerospace and Aviation Technology (28 papers) and Human-Automation Interaction and Safety (26 papers). Lorenzo Pollini collaborates with scholars based in Italy, Germany and United States. Lorenzo Pollini's co-authors include Mario Innocenti, Fabrizio Giulietti, HH Bülthoff, Giampiero Campa, Mario Luca Fravolini, Marcello Napolitano, Marcello R. Napolitano, Brad Seanor, Yu Gu and Marta Niccolini and has published in prestigious journals such as Automatica, IEEE Transactions on Cybernetics and IEEE Transactions on Control Systems Technology.

In The Last Decade

Lorenzo Pollini

122 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lorenzo Pollini Italy 21 893 577 539 476 151 131 1.5k
Mario Innocenti Italy 23 1.2k 1.3× 635 1.1× 1.2k 2.2× 459 1.0× 118 0.8× 167 2.2k
José Luis Sánchez-López Luxembourg 21 847 0.9× 153 0.3× 280 0.5× 859 1.8× 119 0.8× 75 1.5k
Kamesh Subbarao United States 23 1.4k 1.6× 426 0.7× 1.6k 3.0× 361 0.8× 104 0.7× 159 2.4k
H. R. Everett United States 16 577 0.6× 161 0.3× 250 0.5× 556 1.2× 180 1.2× 51 1.1k
Miguel Olivares-Mendez Luxembourg 19 792 0.9× 165 0.3× 310 0.6× 775 1.6× 67 0.4× 112 1.4k
Aditya A. Paranjape United States 15 727 0.8× 394 0.7× 390 0.7× 379 0.8× 106 0.7× 65 1.3k
Douglas G. Macharet Brazil 19 380 0.4× 166 0.3× 152 0.3× 547 1.1× 97 0.6× 76 810
Sung Kyung Hong South Korea 22 556 0.6× 243 0.4× 846 1.6× 212 0.4× 175 1.2× 77 1.4k
Ming Zhu China 20 713 0.8× 157 0.3× 494 0.9× 163 0.3× 55 0.4× 168 1.4k
Animesh Chakravarthy United States 15 729 0.8× 241 0.4× 459 0.9× 598 1.3× 49 0.3× 96 1.2k

Countries citing papers authored by Lorenzo Pollini

Since Specialization
Citations

This map shows the geographic impact of Lorenzo Pollini'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 Lorenzo Pollini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lorenzo Pollini more than expected).

Fields of papers citing papers by Lorenzo Pollini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lorenzo Pollini. 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 Lorenzo Pollini. The network helps show where Lorenzo Pollini may publish in the future.

Co-authorship network of co-authors of Lorenzo Pollini

This figure shows the co-authorship network connecting the top 25 collaborators of Lorenzo Pollini. A scholar is included among the top collaborators of Lorenzo Pollini 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 Lorenzo Pollini. Lorenzo Pollini 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
1.
Ducange, Pietro, et al.. (2025). Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study. CINECA IRIS Institutial research information system (University of Pisa). 1–8.
2.
Pollini, Lorenzo, et al.. (2024). Kalman Filter Based Adaptive Control Allocation. CINECA IRIS Institutial research information system (University of Pisa). 113–118. 1 indexed citations
3.
Pollini, Lorenzo, et al.. (2024). Complementing Human Perception in Remote Site Exploration using Augmented Reality - A Proof of Concept. CINECA IRIS Institutial research information system (University of Pisa).
4.
Gemignani, G, et al.. (2024). An Energy-Aware Decision-Making Scheme for Mobile Robots on a Graph Map Based on Deep Reinforcement Learning. CINECA IRIS Institutial research information system (University of Pisa). 460–466.
5.
Venrooij, Joost, et al.. (2018). Frequency Domain System Identification of a Robinson R44 in Hover. Journal of the American Helicopter Society.
6.
Pollini, Lorenzo, et al.. (2017). Design of a Haptic Helicopter Trainer for Inexperienced Pilots. 1–12.
7.
Antonelli, Gianluca, S. M. Jesus, К. Г. Кебкал, et al.. (2016). The Widely scalable Mobile Underwater Sonar Technology (WiMUST) H2020 project: First year status. OCEANS 2016 - Shanghai. 1–8. 7 indexed citations
8.
Pollini, Lorenzo, et al.. (2015). Augmented Systems for a Personal Aerial Vehicle Using a Civil Light Helicopter Model. 1–9. 2 indexed citations
9.
Bülthoff, HH, et al.. (2014). Development of a 6 dof nonlinear helicopter model for the MPI Cybermotion Simulator. Max Planck Digital Library. 1–12. 1 indexed citations
10.
Pollini, Lorenzo, et al.. (2014). Frequency Domain System Identification of a Light Helicopter in Hover. 1–11. 2 indexed citations
11.
Innocenti, Mario, et al.. (2014). Guidance Augmentation for Improved Target Visibility. AIAA Guidance, Navigation, and Control Conference. 1 indexed citations
12.
Corato, Francesco Di, Mario Innocenti, & Lorenzo Pollini. (2013). Robust vision-aided inertial navigation algorithm via entropy-like relative pose estimation. Gyroscopy and Navigation. 4(1). 1–13. 3 indexed citations
13.
Corato, Francesco Di, Mario Innocenti, & Lorenzo Pollini. (2013). Experimental Evaluation of a Visual-Inertial Navigation System with Guaranteed Convergence. AIAA Guidance, Navigation, and Control (GNC) Conference. 1 indexed citations
14.
Innocenti, Mario, Lorenzo Pollini, & Francesco Di Corato. (2011). An Entropy—Like Approach to Vision—Aided Inertial Navigation. IFAC Proceedings Volumes. 44(1). 13789–13794. 5 indexed citations
15.
Pollini, Lorenzo, et al.. (2010). A comparison of Direct and Indirect Haptic Aiding for Remotely Piloted Vehicles. CINECA IRIS Institutial research information system (University of Pisa). 506–512. 25 indexed citations
16.
Niccolini, Marta, Mario Innocenti, & Lorenzo Pollini. (2010). Near optimal swarm deployment using Descriptor Functions. CINECA IRIS Institutial research information system (University of Pisa). 4952–4957. 9 indexed citations
17.
Pollini, Lorenzo, et al.. (2008). Simulation and Robust Backstepping Control of a Quadrotor Aircraft. AIAA Modeling and Simulation Technologies Conference and Exhibit. 40 indexed citations
18.
Pollini, Lorenzo, et al.. (2006). Vision-Based Autonomous Probe and Drogue Aerial Refueling. 2006 14th Mediterranean Conference on Control and Automation. 1–6. 28 indexed citations
19.
Campa, Giampiero, et al.. (2006). A comparison of Pose Estimation algorithms for Machine Vision based Aerial Refueling for UAVs. 2006 14th Mediterranean Conference on Control and Automation. 1–6. 20 indexed citations
20.
Giulietti, Fabrizio, Lorenzo Pollini, & Mario Innocenti. (2000). Autonomous formation flight. IEEE Control Systems. 20(6). 34–44. 394 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.

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