Brian Coltin

1.4k citations
53 papers · 836 · h-index 15

Impact in

Papers in

Brian Coltin

50 papers receiving 808 citations

Peers

Brian Coltin
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 333
  • Geology 69
  • Aerospace Engineering 289
  • Human-Computer Interaction 48
  • Industrial and Manufacturing Engineering 61
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Qingqing Li China
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Nobuo Kawaguchi Japan
Trey Smith United States
Qing Zhu China
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Citations per field
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Citations per year

Countries citing papers authored by Brian Coltin

Since Specialization
Citations

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

Fields of papers citing papers by Brian Coltin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018105
2
CoBots: robust symbiotic autonomous mobile service robots
201599
3 202372
4 201863
5 201652
6 201041
7 201236
8 201435
9 201134
10 201730
11 201725
12 202324
13 201622
14
Dynamic user task scheduling for mobile robots
201121
15 201420
16 201314
17 201013
18 201413
19 201812
20 201311

About Brian Coltin

Brian Coltin is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Computer Networks and Communications, Artificial Intelligence and Control and Systems Engineering, having authored 53 papers that have together received 836 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (25 papers), Robotic Path Planning Algorithms (16 papers), Advanced Vision and Imaging (7 papers), Optimization and Search Problems (6 papers), Advanced Image and Video Retrieval Techniques (6 papers), Robotics and Automated Systems (5 papers), Transportation and Mobility Innovations (4 papers) and Planetary Science and Exploration (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (333 citations), Geology (69 citations), Aerospace Engineering (289 citations), Human-Computer Interaction (48 citations) and Industrial and Manufacturing Engineering (61 citations). Brian Coltin has collaborated with scholars based in United States, South Korea and Portugal. Frequent co-authors include Manuela Veloso, Trey Smith, Joydeep Biswas, Pyojin Kim, Rodrigo Ventura, H. Jin Kim, Stephanie Rosenthal, Scott McMichael, Andrew Symington and Manuela Veloso. Their work appears in journals such as IEEE Robotics and Automation Letters, Remote Sensing, Acta Astronautica, Autonomous Robots and Remote Sensing of Environment.

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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