Peng Bo | Robotics | Excellence in Scientific Innovation Award

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Dr. Peng Bo | Robotics | Excellence in Scientific Innovation Award

Tsinghua University | China

Dr. Peng Bo is a researcher specializing in control systems, networked autonomous vehicles, and advanced filtering techniques, with a focus on H∞ filtering and event-based control methods for complex networked systems. His work integrates rigorous mathematical modeling, augmented Lyapunov functional approaches, and innovative algorithm design to enhance stability, performance, and reliability in mass-switching autonomous multi-vehicle systems. With a strong foundation in system dynamics and networked control, he has contributed to several high-impact publications, demonstrating a practical yet theoretically robust research approach. His research has influenced both academic developments and real-world applications in intelligent transportation and autonomous systems, reflecting measurable impact through citations and collaborative contributions in his field. Dr. Peng Bo continues to advance methodologies for robust control and optimization in interconnected, dynamic environments.

Citation Metrics (Scopus)

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🟦 Citations   🟥 Documents   🟩 h-index


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Featured Publications

Chien-I Chang | Robotics | Research Excellence Award

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Mr. Chien-I Chang | Robotics | Research Excellence Award

National Chiayi University | Taiwan

Mr. Chien-I Chang is a robotics researcher whose work integrates intelligent systems, autonomous control, and applied computer science to advance next-generation robotic technologies. His research centers on designing adaptive robotic platforms capable of operating in dynamic, unstructured, or human-interactive environments, with a strong emphasis on sensor fusion, machine perception, and real-time decision-making. He has extensive experience developing autonomous navigation algorithms, robotic manipulation systems, and embedded architectures optimized for efficiency, stability, and scalability. His work bridges theoretical modeling with practical engineering, resulting in robotics solutions that are deployable across industrial, educational, and service-oriented settings. Through continuous exploration of mechatronic integration, computer vision, and learning-based control, he aims to enhance robot autonomy and expand practical use cases in daily life. His professional experience includes leading innovative robotics projects, mentoring teams in advanced robotic development, and transforming research outcomes into functional systems with societal impact. He is particularly interested in scalable and modular robotic designs that lower entry barriers for automation and support real-world problem solving. His research philosophy focuses on combining rigorous scientific inquiry with hands-on experimentation to accelerate the evolution of intelligent robotics. Through his academic pursuits and ongoing contributions to the robotics industry, he remains committed to advancing the performance, reliability, and accessibility of robotic technologies while fostering innovation that benefits education, industry, and broader communities.

Profile: Scopus

Featured Publication

Chang, C.-I., (2025). A low-cost autonomous outdoor robot with stabilized controller and deep-learning integrated GPS navigator under end-to-end implementation. Journal of the Chinese Institute of Engineers / Transactions of the Chinese Institute of Engineers, Series A. Advance online publication.

Huaiqu Feng | Robot | Best Researcher Award

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Dr. Huaiqu Feng | Robot | Best Researcher Award

Zhejiang University | China

Huaiqu Feng is an accomplished researcher and engineer from China with a strong academic background in agricultural mechanization engineering and automation. He completed his undergraduate studies in Automation at Hubei Normal University, where he built a solid foundation in control theory, intelligent systems, signal processing, and electronics. He then pursued a master’s degree in Agricultural Mechanization Engineering at Northeast Agricultural University, focusing on areas such as image processing, deep learning, computer vision, and advanced agricultural mechanics. His research interests center on the integration of intelligent control systems and machine vision technologies to improve agricultural equipment and automation. He has contributed to several scientific papers submitted to international SCI-indexed journals, addressing topics such as deep learning-based corn kernel selection and targeted pesticide spraying platforms. His research output includes 20 documents with 431 citations by 376 documents, reflecting his growing impact in the scientific community. His innovative work is also demonstrated through multiple patents for agricultural machinery and software copyrights related to neural network-based prediction and measurement systems. He has received various scholarships and awards for academic excellence and innovation, including recognition in national competitions such as the “Blue Bridge Cup” and the “Huawei Cup.” Beyond his technical achievements, he has demonstrated leadership through active involvement in student organizations, academic committees, and innovation programs, leading teams in robotics and intelligent control system design. His multidisciplinary expertise bridges automation, machine learning, and agricultural engineering, contributing to the advancement of smart farming technologies.

Profile: Scopus

Featured Publications

Feng, Huaiqu (2025). TD-CFD-DPM coupled method for multi-objective optimization of collision pollination parameters in hybrid rice seed production. Smart Agricultural Technology.

Feng, Huaiqu (2025). nUGV-1UAV robot swarms: Low-altitude remote sensing-based decentralized planning framework in-field environments. ISPRS Journal of Photogrammetry and Remote Sensing.

Liu Ziguo | Robotics | Best Researcher Award

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Mr. Liu Ziguo |  Robotics | Best Researcher Award

Chongqing University,  China

Ziguo Liu is a graduate student at the School of Automation, Chongqing University. His research focuses on robot control, multi-agent system control, and prescribed-time control. He has published one SCI-indexed journal article addressing advanced control strategies for multi-agent systems and holds three patents (published or under review). His recent work introduces a finite time-varying gain method to solve rotating containment control problems involving multiple moving leaders. Ziguo Liu’s research contributes to simplifying control system design using polar coordinate approaches and has practical relevance in automation and robotics.

Profile:

📚 Academic Background & Interests:

Ziguo Liu is currently pursuing his graduate studies at Chongqing University. His research focuses on cutting-edge topics in:

  • 🤖 Robot Control

  • 🤝 Multi-Agent System (MAS) Control

  • ⏱️ Prescribed-Time Control

🧪 Research and Innovation Highlights:

  • ✅ Published Journal Article (SCI):
    “Prescribed-Time Rotating Containment Control with Moving Leaders”
    🔗 Read Article

  • 🔬 Research Projects: 1 completed

  • 🧾 Patents: 3 (Published/Under Process)

  • 📘 Books: None

  • 🤝 Industry/Consultancy Projects: None

🚀 Key Contribution:

Ziguo Liu proposed a novel polar coordinate-based method to simplify the rotating containment control problem in MASs. His work enables advanced prescribed-time control strategies using finite time-varying gain, especially useful in coordinating multiple moving robotic leaders.

🌐 Additional Info:

  • 🧑‍🔬 No editorial roles yet

  • 🌍 No international collaborations listed

  • 🎓 No formal professional memberships mentioned yet

Publication:

Finite-gain based prescribed-time rotating containment control for second-order multi-agent systems
Journal of the Franklin Institute, May 2025
DOI: 10.1016/j.jfranklin.2025.107721
Authors: Ziguo Liu, Yujuan Wang, Qing Chen

Yang Yu | Robotics and Automation | Best Researcher Award

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Dr. Yang Yu | Robotics and Automation | Best Researcher Award

Assistant research fellow Jiangsu University China

Yang Yu is an accomplished researcher in agricultural engineering, serving as an Assistant Researcher at Jiangsu University’s School of Agricultural Engineering. With a strong background in agricultural mechanization and informatics, Yang’s expertise lies in intelligent agricultural equipment and precision farming techniques.

Profile

Scopus

🎓 Education:

  • Ph.D. in Agricultural Mechanization Engineering, China Agricultural University (2017-2021)
  • Master’s in Agricultural Informatization, Henan Agricultural University (2015-2017)
  • Bachelor’s in Software Engineering, Henan Agricultural University (2010-2014)

🧑‍🔬 Experience:

  • Assistant Researcher, School of Agricultural Engineering, Jiangsu University (2023-present)
  • Assistant Researcher, Key Laboratory of Intelligent Agricultural Equipment Theory and Technology, Jiangsu University (2022-present)
  • Assistant Researcher, School of Agricultural Engineering, Jiangsu University (2021-2022)
  • Active Post-Doc, Jiangsu University (2022-present)

🔬 Research Interests:

Yang Yu’s research focuses on developing advanced methodologies and technologies in intelligent agricultural equipment, multi-sensor information fusion, and precision farming for crop yield optimization. His work emphasizes efficient, non-clogging harvesting and dynamic soil analysis for tailored agricultural practices.

🏆 Awards:

  • Ongoing contributor to nationally funded agricultural research projects.
  • Participant in prestigious initiatives like the National Natural Science Foundation of China and the National “13th Five-Year” Key R&D Program.

📄 Publications Top Notes:

Publication Title 1, Published in Journal Name (Year), Cited by X articles. [Hyperlink]

Publication Title 2, Published in Journal Name (Year), Cited by X articles. [Hyperlink]
(Full list and citations are customized based on further details)

National Natural Science Foundation of China, High-efficiency Non-clogging Harvesting Method for High-yield Super Rice, 2023-2026.

National Natural Science Foundation of China, Precise Variable-rate Corn Seeding Method, 2021-2024.

Ministry of Science and Technology, Variable Fertilization Operation Condition Detection Technology Device, 2017-2021.