Prof. Wei Ma | Robotics and Automation | Research Excellence Award

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Prof. Wei Ma | Robotics and Automation | Research Excellence Award

Associate Professor | Tianjin University | China

Prof. Wei Ma is a distinguished researcher recognized for significant contributions to underwater glider development within the domain of Robotics and Automation, where Robotics and Automation remain central to his scientific endeavors. As an Associate Professor at Tianjin University, Prof. Wei Ma has advanced Robotics and Automation through intelligent operation, hydrodynamic modelling and control of unmanned marine platforms. His research encompasses the optimization of underwater glider mechanics, variational mode decomposition for marine data processing and model based multi objective control, each contributing to a growing impact on Robotics and Automation applied to ocean engineering. With a record of ten indexed publications and fifteen patents published or under processing, Prof. Wei Ma continues to demonstrate excellence in Robotics and Automation research with high quality outputs featured in reputable journals including Physics of Fluids, Chaos, Ocean Engineering, Journal of Marine Science and Engineering and Journal of Mechanical Engineering Science. His work on air droppable underwater glider water entry, virtual prototype modelling and shape memory alloy based buoyancy systems remains widely noted in Robotics and Automation due to innovative approaches to control, sensing, networking technologies and AI driven data analytics. Prof. Wei Ma further supports the Robotics and Automation community as a reviewer for respected journals, reflecting recognition of his scholarly authority and scientific judgement. His achievements include major technology progress recognition for water surface glider engineering and an outstanding contribution award for glider system product deployment, strengthening the relevance of Robotics and Automation to maritime applications and marine intelligence systems. Through ongoing projects, expanding research themes and growing publication strength, Prof. Wei Ma continues to shape Robotics and Automation innovation with strong societal and technological relevance. Scopus profile of 231 Citations, 35 Documents, 8 h-index.

Profiles: Scopus | ORCID

Featured Publications

1. Yang, P., Wang, Y., Ma, W., Niu, W., Song, Y., & Li, Q. (2025). Fused spatial–temporal graph convolutional networks for ocean currents forecasting using underwater glider measurements. IEEE Journal of Oceanic Engineering.

2. Xi, H., Ma, W., Song, Y., Fa, S., Song, J., & Yang, M. (2025). Energy consumption prediction and endurance optimization for underwater gliders based on data-model fusion. Engineering Applications of Artificial Intelligence.

3. Lyu, G., Wu, S., Song, J., Fa, S., Wang, W., Miao, Z., Gong, F., Ma, W., & Wang, C. (2025). Model and data-driven hydrodynamic identification and prediction for underwater gliders. Physics of Fluids.

4. Ma, W., Wang, Y., Wang, S., Li, G., & Yang, S. (2019). Optimization of hydrodynamic parameters for underwater glider based on the electromagnetic velocity sensor. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Dr. Wu Qiuxuan | Robotics and Automation | Best Researcher Award

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Dr. Wu Qiuxuan | Robotics and Automation | Best Researcher Award

Teacher | Hangzhou Dianzi university | China

Dr. Wu Qiuxuan, an Associate Professor at the School of Automation, Hangzhou Dianzi University, is a distinguished researcher whose expertise and leadership have significantly advanced the field of Robotics and Automation. With a Ph.D. in Control Science and Engineering from Shanghai Jiaotong University, his academic journey reflects a deep commitment to innovation in Robotics and Automation, particularly in the areas of soft robotics, evolutionary learning, and home energy systems. Dr. Wu’s professional experience includes academic and research roles, notably as a visiting scholar at the Australian National University, where he furthered his understanding of intelligent robotic systems. His extensive research on bipedal robots, underwater biomimetic designs, and bio-inspired control algorithms has earned him international recognition. Dr. Wu has authored impactful papers in leading journals such as IEEE Robotics and Automation Letters and Bioinspiration & Biomimetics, contributing to global advancements in Robotics and Automation. His work integrates advanced modeling, deep reinforcement learning, and optimization techniques to enhance robotic adaptability and performance. Over the years, Dr. Wu has received numerous research grants supporting his pioneering studies on service robots, industrial automation, and 3D bioprinting technologies, underscoring his central role in the evolution of Robotics and Automation. With 721 citations, an h-index of 11, and an i10-index of 20, his scholarly influence continues to grow. Dr. Wu’s research skills encompass algorithmic innovation, system optimization, and control engineering, blending theoretical insight with practical application. In conclusion, Dr. Wu Qiuxuan stands as a driving force in Robotics and Automation, whose interdisciplinary expertise continues to shape intelligent systems and inspire the next generation of automation research worldwide.

Profiles: ORCID | Google Scholar

Featured Publications

1. Cai, N., He, M., Wu, Q., & Khan, M. J. (2019). On almost controllability of dynamical complex networks with noises. Journal of Systems Science and Complexity, 32(4), 1125–1139.

2. Chi, X., Liu, B., Niu, Q., & Wu, Q. (2012). Web load balance and cache optimization design based nginx under high-concurrency environment. Proceedings of the Third International Conference on Digital Manufacturing & Automation, 69–73.

3. Wu, Q., Yang, X., Wu, Y., Zhou, Z., Wang, J., Zhang, B., Luo, Y., Chepinskiy, S. A., ... (2021). A novel underwater bipedal walking soft robot bio-inspired by the coconut octopus. Bioinspiration & Biomimetics, 16(4), 046007.

4. Wu, Q., Gu, Y., Li, Y., Zhang, B., Chepinskiy, S. A., Wang, J., Zhilenkov, A. A., ... (2020). Position control of cable-driven robotic soft arm based on deep reinforcement learning. Information, 11(6), 310.

5. Chi, X., Wang, C., Wu, Q., Yang, J., Lin, W., Zeng, P., Li, H., & Shao, M. (2023). A ripple suppression of sensorless FOC of PMSM electrical drive system based on MRAS. Results in Engineering, 20, 101427.