Jing-Li Fu | Robotics | Best Research Article Award

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Prof. Dr. Jing-Li Fu | Robotics | Best Research Article Award

Prof. Dr. Jing-Li Fu | Shandong Vocational University of Foreign Affairs, Zhejiang Sdi-Tech University |  China 

Prof. Dr. Fu Jingli is a distinguished scholar in mathematics and applied mechanics, currently serving as a second-level professor and doctoral supervisor at Zhejiang University of Technology. With a prolific academic career, he has authored over 150 research papers, with more than 100 indexed by SCI and over 70 by EI. His work has been cited over 1,000 times in SCI journals. He is known for advancing the theory of symmetries in dynamical systems. His contributions span theoretical innovation and practical applications, establishing him as a prominent figure in China’s scientific and educational landscape.

Profile

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Education

Prof. Dr. Fu Jingli earned his Ph.D. in a technical field and currently serves as a second-level professor and doctoral supervisor at Zhejiang University of Technology. His academic journey has been marked by rigorous scientific training and specialization in mechanics and dynamical systems. With deep expertise developed through advanced research and mentoring, he has cultivated a strong academic foundation. His education laid the groundwork for hosting several national-level projects and producing highly cited publications. Prof. Fu’s scholarly background reflects a commitment to excellence, both in teaching and research, and has significantly shaped his contributions to science and engineering.

Prof. Dr. Fu Jingli is a senior academic with extensive experience in research, teaching, and supervision. As a second-level professor at Zhejiang University of Technology, he has led five projects funded by the National Natural Science Foundation of China. With a strong research record and mentoring history, he has guided numerous postgraduate and doctoral students. He contributed to both theoretical research and curriculum innovation. His experience also includes collaboration with national educational and scientific bodies, significantly advancing the field of dynamical systems and mathematical modeling. His expertise is reflected in national and provincial awards and a substantial publication record.

Research Focus

Prof. Fu Jingli’s research focuses on Noether symmetries, conserved quantities in nonconservative dynamical systems, and geometric mechanics. His influential 2003 paper in Physics Letters A, co-authored with Li-Qun Chen, has been cited over 50 times, showcasing its impact in theoretical physics. He explores differential equations, variational principles, and symmetry-based analytical mechanics. His work aids in understanding physical systems lacking traditional conservation laws. Through mathematical modeling and theoretical analysis, his contributions bridge pure mathematics with applied science, supporting advances in physics, engineering, and control systems. His research significantly shapes developments in symmetry methods and integrability in modern dynamics.

Awards and Honors

Prof. Fu Jingli has received multiple prestigious honors for his contributions to science and education. He earned a second prize (first place) in the Zhejiang Provincial Science and Technology Award and a second prize in the Natural Science Award from the Ministry of Education (second place). He also received a second prize in the Shanghai Science and Technology Progress Award (second place). In the field of education, he won a first prize in provincial teaching achievements (first place). These accolades reflect his dual excellence in research and pedagogy, marking him as a key contributor to China’s scientific and academic progress.

 Publications Top Notes

Title: Basic Principles of Deformed Objects with Methods of Analytical Mechanics
Journal: Journal of Nonlinear Mathematical Physics
Year: 2024
Citations: 1
Authors: Fu Jingli, others not specified

Title: Lie Symmetries and Conserved Quantities of Static Bertotti–Robinson Spacetime
Journal: Chinese Journal of Physics
Year: 2024
Citations: 2
Authors: Fu Jingli, others not specified

Title: Dynamics Analysis of a Nonlinear Controlled Predator–Prey Model with Complex Poincaré Map
Journal: Nonlinear Analysis: Modelling and Control
Year: 2024
Citations: 1
Authors: Fu Jingli, others not specified

Title: Fractional Hamilton’s Canonical Equations and Poisson Theorem of Mechanical Systems with Fractional Factor
Journal: Mathematics
Year: 2023
Citations: 1
Authors: Fu Jingli, others not specified

Title: Dynamic Analysis of a Phytoplankton-Fish Model with the Impulsive Feedback Control Depending on the Fish Density and Its Changing Rate
Journal: Mathematical Biosciences and Engineering
Year: 2023
Citations: 2
Authors: Fu Jingli, others not specified

Title: Lie Group Analysis Method for Wall Climbing Robot Systems
Journal: Indian Journal of Physics
Year: 2022
Citations: 4
Authors: Fu Jingli, others not specified

Title: Noether Symmetries and Conserved Quantities of Wall Climbing Robot System
Journal: Lixue Xuebao (Chinese Journal of Theoretical and Applied Mechanics)
Year: 2022
Citations: 6
Authors: Fu Jingli, others not specified

Title: A Symplectic Algorithm for Constrained Hamiltonian Systems
Journal: Axioms
Year: 2022
Citations: 4
Authors: Fu Jingli, others not specified

Title: Sensitivity Analysis of Pesticide Dose on Predator-Prey System with a Prey Refuge
Journal: Journal of Applied Analysis and Computation
Year: 2022
Citations: 4
Authors: Fu Jingli, others not specified

Title: On Noether Symmetries and Conserved Quantities of Nonconservative Dynamical Systems
Journal: Physics Letters A
Year: 2003
Citations: 50+
Authors: Jing-Li Fu, Li-Qun Chen

Conclusion

Prof. Dr. Fu Jingli exemplifies academic excellence through his profound research, impactful teaching, and national-level recognition. With over 150 publications and significant citations, his theoretical advancements in dynamical systems have influenced both academia and practical applications. His awards and leadership in major national research projects further reinforce his stature as a scholar of distinction. Through his role as a doctoral supervisor and educator, he continues to nurture the next generation of researchers. His sustained contributions to mathematics and engineering position him as a deserving candidate for honors like the Best Researcher Award.

Muhammad Taha Tariq | Robotics | Best Researcher Award

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Mr. Muhammad Taha Tariq | Robotics | Best Researcher Award

Mr. Muhammad Taha Tariq, Nanjing University of Aeronautics and Astronautics, China.

Muhammad Taha Tariq 🎓 is a dedicated researcher in Control Science and Engineering from Nanjing University of Aeronautics and Astronautics. His focus lies in 🤖 mobile robot path planning, using 🧠 Deep Reinforcement Learning and 🗺️ Large Language Models for dynamic navigation. With nationally funded projects and publications in top-tier venues, he brings innovation, precision, and AI-driven impact to the field. He is an active member of IEEE and ASME, continually pushing the boundaries of robotic intelligence.

🌟 Professional Profile

🎓 Early Academic Pursuits

Muhammad Taha Tariq began his academic journey with a strong inclination towards automation and artificial intelligence. He pursued a Master’s degree in Control Science and Engineering at Nanjing University of Aeronautics and Astronautics, where he built a robust foundation in machine learning, deep learning, and robotics. His early academic exposure shaped his research orientation toward practical innovation in intelligent systems, especially mobile robots and their navigation capabilities.

💼 Professional Endeavors

As a student researcher, Taha has embarked on ambitious projects combining theoretical excellence with practical implementations. He is affiliated with leading research programs and has actively contributed to funded projects, focusing on Deep Reinforcement Learning and Large Language Models. Despite being at an early stage of his career, his engagement with prestigious funding programs in China highlights his dedication and potential in academic research.

🔬 Contributions and Research Focus On Robotics 

Taha’s research specializes in mobile robot path planning, emphasizing dynamic environments and obstacle avoidance. His 2023–2024 project introduced a Deep Reinforcement Learning-based framework to calculate collision probabilities in real-time. In 2024–2025, he developed an innovative system that integrates LLMs for dynamic waypoint generation, achieving a 95.5% success rate and average task completion in 9.43 seconds. These contributions are both nationally funded and recognized through publications and technical demonstrations.

🌍 Impact and Influence

Though early in his career, Taha’s work reflects a deep commitment to open science. He provides preprints on arXiv, demonstration videos on YouTube, and open-source code on GitHub, fostering transparency and reproducibility in AI research. His methods are not only academically sound but also scalable for real-world robotic applications, influencing future trends in intelligent automation systems.

🏆 Awards and Honors

Taha has been nominated for the Best Researcher Award, a testament to his innovative work in automation and robotics. His selection is supported by successful national grants and active contributions to IEEE and ASME student communities.

📚 Academic Citations

As of now, Muhammad Taha Tariq does not report a citation index, but with publications accepted in journals like Expert Systems with Applications and conference presentations at the WRC Symposium, his research is gaining scholarly visibility and is poised to attract academic citations in the near future.

🚀 Legacy and Future Contributions

Muhammad Taha Tariq’s journey reflects a promising trajectory toward becoming a leading AI researcher. His legacy will likely include scalable frameworks for autonomous navigation and AI integration. Moving forward, he envisions enhancing robot-environment interaction using cutting-edge language models, contributing to safer and more efficient robotics applications in industries and smart cities.

📚Publications Top Notes

📄 1. Deep Reinforcement Learning-Based Path Planning with Dynamic Collision Probability for Mobile Robots