About

Xingjian Jing is a distinguished researcher whose work bridges nonlinear dynamics, bio-inspired engineering, and robotics, making profound contributions across multiple interconnected fields. He is perhaps best known for pioneering the X-structure/mechanism approach to beneficial nonlinear design, a landmark 2022 framework that fundamentally reframes how engineers understand and harness nonlinearity in structural and dynamic systems — a paper that has already garnered 124 citations. Alongside this theoretical foundation, his 2019 exploration of nonlinear benefits in engineering further established him as a leading voice challenging conventional linear assumptions in mechanical design. Jing's robotics research is equally impactful, encompassing bio-inspired vibration isolation, anti-impact manipulators for capturing non-cooperative spacecraft (75 citations), biomimetic underwater robots, and tracked mobile platforms featuring innovative passive suspension systems inspired by biological structures. His work on self-powered monitoring through triboelectric nanogenerators integrated with vibration isolators (58 citations) reflects a forward-thinking fusion of energy harvesting and smart sensing. Additionally, his sensor fusion algorithm for MARG orientation estimation (75 citations) has found direct application in robot teleoperation. Collectively, Jing's research portfolio — spanning theoretical mechanics to practical robotic systems — demonstrates a rare capacity to translate fundamental scientific insight into real-world engineering innovation.

Research Focus

Key Achievements

13
H-Index
30
Papers
683
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
The X-structure/mechanism approach to beneficial nonlinear design in engineering
124 citations · 2022
📈 Most Prolific Year: 2024 (6 Papers)
🤝 Key Collaborators: 57
🏛 Institutions: City University of Hong Kong, Hong Kong Polytechnic University, University of Sheffield, Chinese Academy of Sciences, Shenyang Institute of Automation, Chinese Academy of Social Sciences

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago