João Sequeira

61 papers receiving 454 citations

Peers

João Sequeira
Comparison fields: 5 of 108
  • Computer Vision and Pattern Recognition 121
  • Human-Computer Interaction 23
  • Social Psychology 80
  • Control and Systems Engineering 87
  • Artificial Intelligence 116
Replace Jonathan M. Aitken with:
Jonathan M. Aitken United Kingdom
Phillip Walker United States
Ramviyas Parasuraman United States
Mikhail Medvedev Russia
Futoshi Naya Japan
Patrick Benavidez United States
Francisco J. Rodríguez-Lera Spain
Kathryn Kasmarik Australia
Brennan Sellner United States
Dominik Joho Germany
João Sequeira relative to Jonathan M. Aitken United Kingdom Jonathan M. Aitken's profile →
Citations per field
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Jonathan M. Aitken · 1×
Citations per year

Countries citing papers authored by João Sequeira

Since Specialization
Citations

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

Fields of papers citing papers by João Sequeira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside João Sequeira, 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 João Sequeira Line = papers co-authored together João Sequeira links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201374
2 201032
3 201928
4 201425
5 200922
6 200721
7 201520
8 201120
9 200617
10
The Development of a Robotic System for Maintenance and Inspection of Power Lines
200317
11 202214
12 200314
13 201814
14 202013
15 201012
16 201710
17 20108
18 20076
19 20156
20 20045

About João Sequeira

João Sequeira is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Mechanical Engineering and Biomedical Engineering, having authored 72 papers that have together received 474 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (16 papers), Control and Dynamics of Mobile Robots (9 papers), Social Robot Interaction and HRI (8 papers), Robotics and Sensor-Based Localization (8 papers), Modular Robots and Swarm Intelligence (7 papers), Robotic Locomotion and Control (6 papers), Ethics and Social Impacts of AI (5 papers) and Underwater Vehicles and Communication Systems (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (121 citations), Human-Computer Interaction (23 citations), Social Psychology (80 citations), Control and Systems Engineering (87 citations) and Artificial Intelligence (116 citations). João Sequeira has collaborated with scholars based in Portugal, France and United Kingdom. Frequent co-authors include Fernándo Alonso-Martín, Miguel Á. Salichs, María Malfáz, Matthijs T. J. Spaan, M. O. Tokhi, Endre E. Kádár, G.S. Virk, Rodrigo Ventura, Antonios Tsourdos and Pedro U. Lima. Their work appears in journals such as PeerJ Computer Science, Sensors, Advances in experimental medicine and biology, Electronics and Language Resources and Evaluation.

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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