Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559

Robin Chhabra | Robotics | Best Researcher Award

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šŸŒŸProf Dr. Robin Chhabra, Robotics, Best Researcher AwardšŸ†

Ā Professor at Carleton University, Canada

Dr. Raj Chhabra is a distinguished researcher and educator specializing in robotics and control systems. His expertise, particularly in the geometric dynamics and control of multibody systems, is evident through a rich academic and professional journey. As a faculty member at Carleton University, he actively mentors students, contributing significantly to the field of space robotics.

Author Metrics:

Dr. Chhabra’s research impact is evident through robust author metrics, including multiple papers published and under review in esteemed journals. The citations and engagement with the academic community underscore the significance of his work in the field.

Scopus Profile

Orcid Profile

Google Scholar Profile

  • Citations:
    • Total Citations: 321
    • Citations in the last index: 226
  • h-index:
    • h-index: 9
  • i10-index:
    • i10-index: 9

These metrics reflect the impact and influence of Dr. Chhabra’s scholarly work. The h-index of 9 indicates that there are 9 papers that have each been cited at least 9 times, demonstrating a significant and lasting impact in the academic community. The i10-index of 9 signifies that Dr. Chhabra has 9 publications with at least 10 citations each. These metrics collectively showcase the recognition and reach of his contributions within the research community.

Education:

Dr. Chhabra holds a Ph.D. in Robotics and Control from Carleton University, providing him with a robust foundation for addressing intricate challenges in the realm of robotics.

Research Focus:

His research primarily centers around the geometric dynamics and control of multibody systems, emphasizing applications in space robotics. Dr. Chhabra explores innovative formulations to address complex interactions and dynamics inherent in these systems.

Professional Journey:

After completing his Ph.D., Dr. Chhabra transitioned into academia, joining Carleton University as a faculty member. His professional journey has been marked by active involvement in research, mentorship, and teaching, along with industry engagement through mentorship programs at MDA Robotics.

Honors & Awards:

Dr. Chhabra’s contributions have been recognized with nominations for the University Medal and various awards, highlighting his outstanding research and mentorship in the academic community.

Publications Top Noted & Contributions:

With a prolific publication record, Dr. Chhabra has authored numerous research papers in reputable journals. His work spans topics such as singularity-free dynamics, nonlinear control, and geometric formulations for multibody systems, contributing significantly to the advancement of knowledge in robotics.

SuRFR: A fast recursive simulator for soft manipulators with discrete joints on SE(3)
  1. Objective:
    • Develop a fast, recursive, and parameterization-free formulation for the dynamics of soft robots.
    • Model these systems as multi-body systems consisting of both rigid and flexible bodies connected with discrete joints.
  2. Methodology:
    • Combine the recursive Newtonā€“Euler equation for rigid bodies with Partial Differential Equations (PDEs) on the Special Euclidean group SE(3) to model dynamic Cosserat rods.
    • Utilize the proposed inverse dynamics to recursively determine the system’s response and joint torques, given a joint-space trajectory.
    • Employ forward dynamics to determine the system’s motion, taking into account known joint torques and external forces.
  3. Challenges and Solutions:
    • Address the challenges introduced by the inclusion of flexible bodies, requiring the solution of a coupled set of PDEs with separated Boundary Conditions (BCs).
    • Develop a shooting-method-based BC solver to consolidate BCs into one point.
    • Implement a numerical framework based on a finite difference method to spatially integrate these equations using a geometrically exact integrator on SE(3).
  4. Implementation:
    • Implement the proposed algorithms into a software library named SimUlator for Rigidā€“Flexible Robots (SuRFR).
  5. Application:
    • Study the response of a manipulator with a soft gripper using SuRFR.
  6. Reference:

“On the guidance, navigation and control of in-orbit space robotic missions: A survey and prospective vision” (2021)
Authors: B.M. Moghaddam, R. Chhabra
Journal: Acta Astronautica (Volume 184, Pages 70-100)
This survey paper comprehensively addresses the guidance, navigation, and control aspects of in-orbit space robotic missions, offering valuable insights and a forward-looking vision.

“Holistic system modeling in mechatronics” (2011)
Authors: R. Chhabra, M.R. Emami
Journal: Mechatronics (Volume 21, Issue 1, Pages 166-175)
Dr. Chhabra, along with co-author M.R. Emami, contributes to mechatronics by presenting a holistic system modeling approach, demonstrating the interdisciplinary nature of this field.

“A generalized exponential formula for forward and differential kinematics of open-chain multi-body systems” (2014)
Authors: R. Chhabra, M.R. Emami
Journal: Mechanism and Machine Theory (Volume 73, Pages 61-75)
This paper introduces a generalized exponential formula, authored by Dr. Chhabra and M.R. Emami, addressing forward and differential kinematics in open-chain multi-body systems.

“A holistic concurrent design approach to robotics using hardware-in-the-loop simulation” (2013)
Authors: R. Chhabra, M.R. Emami
Journal: Mechatronics (Volume 23, Issue 3, Pages 335-345)
Dr. Chhabra contributes to the field of robotics with a holistic concurrent design approach, leveraging hardware-in-the-loop simulation for efficient system development.

“A mission architecture for on-orbit servicing industrialization” (2021)
Authors: P. Rousso, S. Samsam, R. Chhabra
Conference: 2021 IEEE Aerospace Conference (Pages 1-14)
Dr. Chhabra collaborates on a mission architecture paper for on-orbit servicing industrialization, presented at the IEEE Aerospace Conference, showcasing practical applications of his research.

Research Timeline:

Dr. Chhabra’s research journey has evolved from foundational exploration during his Ph.D. to leading diverse projects as a faculty member. The timeline reflects his progression in addressing real-world challenges in space robotics.

Conference Participation:

Actively engaging in conferences, Dr. Chhabra presents his research findings and stays connected with the scientific community. These conferences serve as crucial platforms for exchanging ideas and staying updated on the latest developments in robotics and control systems.