Philip Long

629 citations
32 papers · 248 indexed · h-index 8

Impact in

Papers in

Philip Long

27 papers receiving 239 citations

Peers

Philip Long
Comparison fields: 5 of 47
  • Control and Systems Engineering 171
  • Industrial and Manufacturing Engineering 39
  • Acoustics and Ultrasonics 3
  • Medical Laboratory Technology 5
  • Computer Vision and Pattern Recognition 49
Replace Matteo Parigi Polverini with:
Matteo Parigi Polverini Italy
Marco Faroni Italy
Ivan Lundberg Switzerland
Harald Staab Germany
Christopher Parlitz Germany
Yunfei Dong China
Riccardo Caccavale Italy
Alexander Alspach United States
S. Reza Ahmadzadeh United States
Dorothea Koert Germany
Philip Long relative to Matteo Parigi Polverini Italy Matteo Parigi Polverini's profile →
Citations per field
00.5×1.5×
Matteo Parigi Polverini · 1×
Citations per year

Countries citing papers authored by Philip Long

Since Specialization
Citations

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

Fields of papers citing papers by Philip Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
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8 20223
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11 20219
12 201920
13 201950
14 201912
15 201813
16 20174
17 20172
18 201421
19 20121
20 20063

About Philip Long

Philip Long is a scholar working on Control and Systems Engineering, Medical Laboratory Technology, Biomedical Engineering, Computer Vision and Pattern Recognition and Instrumentation, having authored 32 papers that have together received 248 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (17 papers), Robotic Mechanisms and Dynamics (11 papers), Robotic Locomotion and Control (10 papers), Robotic Path Planning Algorithms (6 papers), Modular Robots and Swarm Intelligence (3 papers), Power Line Communications and Noise (3 papers), Reinforcement Learning in Robotics (3 papers) and Soft Robotics and Applications (3 papers). The work is most often cited by research in Control and Systems Engineering (171 citations), Industrial and Manufacturing Engineering (39 citations), Acoustics and Ultrasonics (3 citations), Medical Laboratory Technology (5 citations) and Computer Vision and Pattern Recognition (49 citations). Philip Long has collaborated with scholars based in United States, Ireland and France. Frequent co-authors include Taşkın Padır, Stéphane Caro, Wisama Khalil, Tahir Rasheed, Damien Chablat, Christine Chevallereau, Alexis Girin, Philippe Martinet, Michael R. Gleeson and Maria Chiara Leva. Their work appears in journals such as IEEE Transactions on Vehicular Technology, Journal of Optics, Mechanism and Machine Theory, Robotica and Robotics.

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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