Pedro U. Lima

167 papers receiving 2.0k citations

Peers

Pedro U. Lima
Comparison fields: 5 of 121
  • Computer Vision and Pattern Recognition 752
  • Computer Networks and Communications 613
  • Aerospace Engineering 565
  • Control and Systems Engineering 474
  • Artificial Intelligence 492
Replace Luca Iocchi with:
Luca Iocchi Italy
Mo Jamshidi United States
Shinpei Kato Japan
Francesco Amigoni Italy
T. K. Satish Kumar United States
Patrick Doherty Sweden
Geoffrey A. Hollinger United States
Andrew Howard United States
John Schulman United States
Mo Chen United States
Pedro U. Lima relative to Luca Iocchi Italy Luca Iocchi's profile →
Citations per field
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Citations per year

Countries citing papers authored by Pedro U. Lima

Since Specialization
Citations

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

Fields of papers citing papers by Pedro U. Lima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro U. Lima. 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 Pedro U. Lima. The network helps show where Pedro U. Lima may publish in the future.

Co-authors

The 25 scholars most cited alongside Pedro U. Lima, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Pedro U. Lima Line = papers co-authored together Pedro U. Lima links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 183 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2001100
2 201470
3 201068
4 200568
5 201263
6 201347
7 200245
8 200544
9 200643
10 200142
11 201642
12 201241
13 201540
14 201439
15 201139
16 201537
17 201533
18 200430
19 200730
20 201229

About Pedro U. Lima

Pedro U. Lima is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Computer Networks and Communications and Control and Systems Engineering, having authored 183 papers that have together received 2.2k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (36 papers), Robotic Path Planning Algorithms (30 papers), Modular Robots and Swarm Intelligence (26 papers), Reinforcement Learning in Robotics (23 papers), Petri Nets in System Modeling (22 papers), Distributed Control Multi-Agent Systems (22 papers), Target Tracking and Data Fusion in Sensor Networks (13 papers) and Formal Methods in Verification (13 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (752 citations), Computer Networks and Communications (613 citations), Aerospace Engineering (565 citations), Control and Systems Engineering (474 citations) and Artificial Intelligence (492 citations). Pedro U. Lima has collaborated with scholars based in Portugal, United States and Switzerland. Frequent co-authors include Paulo Tabuada, Meysam Basiri, Matthijs T. J. Spaan, George J. Pappas, Aamir Ahmad, Felix Schill, Dario Floreano, Dejan Milutinović, Carlos Marques and Rodrigo Ventura. Their work appears in journals such as Robotics and Autonomous Systems, IEEE Robotics & Automation Magazine, IEEE Transactions on Robotics, Applied Sciences and IEEE Robotics and Automation Letters.

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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