Chee–Meng Chew

3.0k total citations
135 papers, 2.2k citations indexed

About

Chee–Meng Chew is a scholar working on Biomedical Engineering, Control and Systems Engineering and Mechanical Engineering. According to data from OpenAlex, Chee–Meng Chew has authored 135 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Biomedical Engineering, 46 papers in Control and Systems Engineering and 30 papers in Mechanical Engineering. Recurrent topics in Chee–Meng Chew's work include Robotic Locomotion and Control (45 papers), Prosthetics and Rehabilitation Robotics (43 papers) and Robot Manipulation and Learning (29 papers). Chee–Meng Chew is often cited by papers focused on Robotic Locomotion and Control (45 papers), Prosthetics and Rehabilitation Robotics (43 papers) and Robot Manipulation and Learning (29 papers). Chee–Meng Chew collaborates with scholars based in Singapore, United States and China. Chee–Meng Chew's co-authors include Gill A. Pratt, Jerry Pratt, Keng Peng Tee, Etienne Burdet, Theodore E. Milner, Y. Zheng, Geok Soon Hong, David W. Franklin, Mitsuo Kawato and Rieko Osu and has published in prestigious journals such as Journal of Neuroscience, IEEE Transactions on Industrial Electronics and Journal of Biomechanics.

In The Last Decade

Chee–Meng Chew

130 papers receiving 2.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chee–Meng Chew Singapore	23	1.4k	636	406	319	259	135	2.2k
Sang-Ho Hyon Japan	24	1.7k 1.2×	880 1.4×	129 0.3×	300 0.9×	149 0.6×	98	2.1k
Katja Mombaur Germany	23	1.6k 1.1×	670 1.1×	190 0.5×	109 0.3×	155 0.6×	134	2.2k
Philippe Poignet France	30	1.6k 1.1×	1.2k 1.8×	215 0.5×	649 2.0×	271 1.0×	201	3.1k
Xingang Zhao China	29	1.4k 1.0×	723 1.1×	901 2.2×	202 0.6×	144 0.6×	212	3.0k
Gill A. Pratt United States	21	3.2k 2.2×	1.6k 2.5×	209 0.5×	586 1.8×	202 0.8×	39	3.9k
H. Hemami United States	27	1.8k 1.3×	1.4k 2.2×	444 1.1×	314 1.0×	130 0.5×	138	2.8k
Zhiwei Luo Japan	23	1.3k 0.9×	527 0.8×	271 0.7×	322 1.0×	195 0.8×	145	1.8k
Michael Mistry United Kingdom	26	1.8k 1.3×	1.6k 2.5×	152 0.4×	459 1.4×	251 1.0×	80	2.6k
Caihua Xiong China	31	1.8k 1.2×	953 1.5×	324 0.8×	619 1.9×	109 0.4×	177	2.9k
Thomas G. Sugar United States	28	2.6k 1.8×	786 1.2×	187 0.5×	417 1.3×	102 0.4×	116	3.4k

Countries citing papers authored by Chee–Meng Chew

Since Specialization

Citations

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

Fields of papers citing papers by Chee–Meng Chew

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chee–Meng Chew. 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 Chee–Meng Chew. The network helps show where Chee–Meng Chew may publish in the future.

Co-authorship network of co-authors of Chee–Meng Chew

This figure shows the co-authorship network connecting the top 25 collaborators of Chee–Meng Chew. A scholar is included among the top collaborators of Chee–Meng Chew based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chee–Meng Chew. Chee–Meng Chew is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Pan, Quan-Ke, et al.. (2025). Energy-Efficient Distributed Heterogeneous Hybrid Flow-Shop Scheduling Using Graph Neural Network and Deep Reinforcement Learning. IEEE Transactions on Automation Science and Engineering. 23. 3619–3635.

Pan, Quan-Ke, et al.. (2025). An End-to-End Framework for Energy-Efficient Cascaded Dual-Shop Collaborative Scheduling With Mating Operations. IEEE Transactions on Cybernetics. 55(10). 4929–4942. 1 indexed citations

Zhou, Wenbo, et al.. (2025). A meta-learning enhanced framework for prediction and control of shield-tunneling-induced ground deformation under data scarcity. Transportation Geotechnics. 56. 101779–101779.

Liang, Wenyu, et al.. (2024). Unknown Object Retrieval in Confined Space through Reinforcement Learning with Tactile Exploration. 10881–10887. 1 indexed citations

Tan, Shaohua, et al.. (2024). Deep Reinforcement Learning Based Tractor-Trailer Tracking Control. 3147–3153. 1 indexed citations

Zhang, Guorong, Yujie Xu, Chee–Meng Chew, & Mingyu Fu. (2024). Discrete-Time Sliding Mode-Based Finite-Time Trajectory Tracking Control of Underactuated Surface Vessels With Large Sampling Periods. IEEE Transactions on Intelligent Transportation Systems. 25(12). 21545–21558. 6 indexed citations

Li, Zhibin, et al.. (2023). Real-time terrain anomaly perception for safe robot locomotion using a digital double framework. Robotics and Autonomous Systems. 169. 104512–104512. 1 indexed citations

Huang, Jin, et al.. (2023). Configuration and Design Schemes of Environmental Sensing and Vehicle Computing Systems for Automated Driving: A Review. IEEE Sensors Journal. 23(14). 15305–15320. 9 indexed citations

Hu, Manjiang, Yougang Bian, Chee–Meng Chew, et al.. (2023). Integrated Design Framework for Perception System of Automated Electric Vehicles With Enhanced Sensor Reusability. IEEE Transactions on Transportation Electrification. 10(2). 3075–3091. 1 indexed citations

10.

Chen, Ye‐Hwa, et al.. (2023). New Framework for Fuzzy Logic Reasoning: A Robust Control Theoretic Approach. International Journal of Fuzzy Systems. 26(2). 463–481. 2 indexed citations

11.

Chen, Ye‐Hwa, et al.. (2023). Fuzzy Reasoning Based on Truth-Value Progression: A Control-Theoretic Design Approach. International Journal of Fuzzy Systems. 25(4). 1559–1578. 4 indexed citations

12.

Chuah, Meng Yee, et al.. (2022). Traversability analysis with vision and terrain probing for safe legged robot navigation. Frontiers in Robotics and AI. 9. 887910–887910. 9 indexed citations

13.

Chew, Chee–Meng, et al.. (2020). Towards More Possibilities: Motion Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots. IEEE Robotics and Automation Letters. 5(2). 3723–3730. 18 indexed citations

14.

Pan, Liang, Chee–Meng Chew, & Gim Hee Lee. (2019). PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds.. arXiv (Cornell University). 5 indexed citations

15.

Hong, Geok Soon, et al.. (2014). A directed graph model for distributed sequential control.

16.

Zhang, Qizhi, et al.. (2010). Iterative learning control for biped walking. National University of Singapore. 237–241. 3 indexed citations

17.

Zheng, Y. & Chee–Meng Chew. (2009). A numerical solution to the ray-shooting problem and its applications in robotic grasping. National University of Singapore. 2080–2085. 8 indexed citations

18.

Burdet, Etienne, Keng Peng Tee, Iven Mareels, et al.. (2005). Stability and motor adaptation in human arm movements. Biological Cybernetics. 94(1). 20–32. 107 indexed citations

19.

Zhou, Wei, Chee–Meng Chew, & Geok Soon Hong. (2004). Property analysis for series MR-fluid damper actuator system. National University of Singapore. 4 indexed citations

20.

Tee, Keng Peng, Etienne Burdet, Chee–Meng Chew, & Theodore E. Milner. (2003). Investigating motor adaptation to stable and unstable tasks using haptic interfaces, EMG and fMRI. Society of Instrument and Control Engineers of Japan. 2003. 29–29. 1 indexed citations

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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