Weiwei Wan
Impact in
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- Robot Manipulation and Learning
- Robotic Mechanisms and Dynamics
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- Manufacturing Process and Optimization
- Advanced Manufacturing and Logistics Optimization
Papers in ⓘ
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- Robot Manipulation and Learning 121
- Robotic Mechanisms and Dynamics 34
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- Robotic Path Planning Algorithms 53
- Co-authors
- Kensuke Harada (114 shared papers)Rui Fukui (11 shared papers)Kazuyuki Nagata (15 shared papers)Yukiyasu Domae (16 shared papers)Fumio Kanehiro (3 shared papers)Keisuke Koyama (19 shared papers)Yasuo Kuniyoshi (6 shared papers)Weibo Huang (2 shared papers)
In The Last Decade
Weiwei Wan
152 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 84
- Control and Systems Engineering 1.1k
- Industrial and Manufacturing Engineering 369
- Computer Vision and Pattern Recognition 596
- Human-Computer Interaction 79
- Biomedical Engineering 558
Countries citing papers authored by Weiwei Wan
This map shows the geographic impact of Weiwei Wan'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 Weiwei Wan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weiwei Wan more than expected).
Fields of papers citing papers by Weiwei Wan
This network shows the impact of papers produced by Weiwei Wan. 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 Weiwei Wan. The network helps show where Weiwei Wan may publish in the future.
Co-authors
The 25 scholars most cited alongside Weiwei Wan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 173 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 94 | |
| 2 | 2017 | 70 | |
| 3 | 2019 | 46 | |
| 4 | 2020 | 45 | |
| 5 | 2017 | 43 | |
| 6 | 2017 | 43 | |
| 7 | 2017 | 41 | |
| 8 | 2020 | 39 | |
| 9 | 2017 | 38 | |
| 10 | 2021 | 38 | |
| 11 | 2020 | 36 | |
| 12 | 2019 | 35 | |
| 13 | 2017 | 33 | |
| 14 | 2012 | 32 | |
| 15 | 2018 | 28 | |
| 16 | 2020 | 27 | |
| 17 | 2016 | 27 | |
| 18 | 2016 | 27 | |
| 19 | 2019 | 26 | |
| 20 | 2022 | 25 |
About Weiwei Wan
Weiwei Wan is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Biomedical Engineering, Mechanical Engineering and Industrial and Manufacturing Engineering, having authored 173 papers that have together received 1.7k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (121 papers), Robotic Path Planning Algorithms (53 papers), Soft Robotics and Applications (45 papers), Robotic Mechanisms and Dynamics (34 papers), Manufacturing Process and Optimization (29 papers), Robotics and Sensor-Based Localization (26 papers), Modular Robots and Swarm Intelligence (23 papers) and Teleoperation and Haptic Systems (17 papers). The work is most often cited by research in Control and Systems Engineering (1.1k citations), Industrial and Manufacturing Engineering (369 citations), Computer Vision and Pattern Recognition (596 citations), Human-Computer Interaction (79 citations) and Biomedical Engineering (558 citations). Weiwei Wan has collaborated with scholars based in Japan, China and Hong Kong. Frequent co-authors include Kensuke Harada, Rui Fukui, Kazuyuki Nagata, Yukiyasu Domae, Fumio Kanehiro, Keisuke Koyama, Yasuo Kuniyoshi, Weibo Huang, Hong Liu and Natsuki Yamanobe. Their work appears in journals such as IEEE Robotics and Automation Letters, Advanced Robotics, IEEE Transactions on Automation Science and Engineering, IEEE Access and Assembly Automation.
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.