Background Information: 

Dr. Wei Zhang is an assistant professor with the Eastern Institute of Technology (EIT), Ningbo, China. He received his Ph.D. degree from National University of Singapore in 2021. His main research interests are robot navigation, learning-based control, and intelligent fault diagnosis for unmanned systems. In 2023, He was recognized as one of Top 2% Scientists Worldwide (2022), as ranked by Stanford University. During his studies at Harbin Institute of Technology, he was awarded the National Scholarship three times. He has published several influential papers in his research field, with two first-authored papers cited more than 1,000 times each, and one of the first-authored papers was selected as one of China's 100 Most Influential International Academic Papers by the Institute of Scientific and Technical Information of China. He served as the Session Chair at the Motion and Path Planning II session of ICRA 2021. He has served as a reviewer for IEEE TASE, IEEE TIE, MSSP, ESWA and IROS, etc.

Research Field:

My research primarily centers around the development and enhancement of intelligent control and intelligent health monitoring of unmanned systems, including:

1 Learning-based control

2 Mapless navigation for mobile robots

3 Sim-to-real Transfer in robotics

4 Fault diagnosis for unmanned systems

Educational Background:

2017-2021: PhD. (majoring in Mechanical Engineering), Department of Mechanical Engineering at National University Singapore

2015-2017: Master (majoring in Mechatronics Engineering), School of Mechatronics Engineering at Harbin Institute of Technology

2011-2015: Bachelor (majoring Mechanical Design, Manufacturing and Automation), School of Mechatronics Engineering at Harbin Institute of Technology.

Work Experience:

2024-Present: Assistant Professor, College of Information Science and Technology at Eastern Institute of Technology (EIT), Ningbo, China.

2021-2024: Research Fellow, Department of Mechanical Engineering at National University Singapore

Academic Part-time Jobs (Partial):

2021：Session Chair at ICRA 2021（session: Motion and Path Planning II）

Awards and Honors:

2023: Top 2% Scientists Worldwide (2022)

2019: Top 100 International Influential Academic Papers in China (First Author)

2016: National Scholarship

2013: National Scholarship

2012: National Scholarship

Representative Works:

General Information

Published more than 10 papers, including two first-authored papers with more than 1,000 citations each.

Works Information and Citation Data

Google Scholar:

https://scholar.google.com.sg/citations?user=Z7u9yEoAAAAJ&hl=zh-CN

1. Zhang W, Zhang Y, Liu N, et al. IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(6): 5451-5461.

2. Zhang W, Liu N, Zhang Y. Learn to navigate maplessly with varied LiDAR configurations: A support point-based approach[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1918-1925.

3. Zhang W, Zhang Y, Liu N. Danger-aware adaptive composition of drl agents for self-navigation[J]. Unmanned Systems, 2021, 9(01): 1-9.

4. Liu N, Ren K, Zhang W, et al. An evolutional algorithm for automatic 2D layer segmentation in laser-aided additive manufacturing[J]. Additive Manufacturing, 2021, 47: 102342.

5. Zhang W, Zhang Y F. Behavior switch for DRL-based robot navigation[C]//2019 IEEE 15th International Conference on Control and Automation (ICCA). IEEE, 2019: 284-288.

6. Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical systems and signal processing, 2018, 100: 439-453.

7. Chen Y, Peng G, Xie C, et al. ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis[J]. Neurocomputing, 2018, 294: 61-71.

8. Li C, Zhang W E I, Peng G, et al. Bearing fault diagnosis using fully-connected winner-take-all autoencoder[J]. IEEE Access, 2017, 6: 6103-6115.

9. Zhang W, Peng G, Li C, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425.

10. Zhang W, Peng G, Li C. Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input[C]//MATEC web of conferences. EDP Sciences, 2017, 95: 13001.