Asra Sarwat | Robotics | Research Excellence Award

Published on by Technology Scientists

Research Excellence Award

                   Asra Sarwat
Affiliation Harbin Institute of Technology
Country Australia
Scopus ID 58956274200
Documents 3
Citations 5
h-index 1
Subject Area Robotics
Event Technology Scientists Awards
ORCID 0009-0002-8298-843X

Asra Sarwat, affiliated with Harbin Institute of Technology, has contributed to robotics research through studies emphasizing nonlinear control, adaptive control methodologies, and intelligent motion control systems. The presented academic profile summarizes research achievements, publication contributions, scholarly impact, and the relevance of these accomplishments to recognition through the Technology Scientists Awards. [1]

Abstract

This article presents an overview of the academic profile of Asra Sarwat, highlighting research contributions within robotics and intelligent control systems. The researcher has explored adaptive backstepping control, fuzzy logic integration, nonlinear robotic control, and feedback linearization for articulated mechanisms and robotic hands. These studies contribute to reliable motion control, improved system stability, and enhanced automation performance. Although currently representing an early publication portfolio, the research demonstrates methodological rigor and practical engineering relevance. The documented publications, citation metrics, and scholarly activities collectively indicate meaningful participation in robotics research and support consideration for recognition through the Technology Scientists Awards. [1]

Keywords

Robotics, Adaptive Control, Backstepping Control, Fuzzy Logic, Nonlinear Control, Motion Control, Robotic Hand, Feedback Linearization, Sliding Mode Control, Intelligent Systems.

Introduction

Robotics research increasingly combines intelligent algorithms with advanced nonlinear control methods to improve autonomous operation, precision, stability, and safety. Asra Sarwat’s published studies investigate adaptive and robust control approaches for articulated robotic systems, contributing practical methodologies that support reliable motion planning and intelligent robotic manipulation across engineering applications. [2]

Research Profile

The research profile demonstrates specialization in robotics, nonlinear dynamics, adaptive control, and intelligent automation. Publications emphasize controller development using fuzzy logic, backstepping methods, feedback linearization, and high-order sliding mode control. These topics collectively address motion accuracy, system robustness, and real-time implementation challenges encountered in robotic engineering. [3]

Research Contributions

The published research introduces adaptive and nonlinear control strategies for multi-degree-of-freedom articulated systems and robotic hands. These contributions evaluate controller performance under dynamic conditions while improving stability, tracking precision, and operational efficiency. The investigations support continued advancement of intelligent robotic control methodologies for practical engineering environments. [2]

Publications

  • Adaptive Backstepping Control with Fuzzy Logic for Real-Time Motion Control of a Multi-DoF Articulated Systems. DOI: https://doi.org/10.1016/j.robot.2026.105326
  • Adaptive backstepping control with fuzzy logic for real-time motion control of a multi-DoF articulated systems. ScienceDirect publication describing adaptive robotic control methodologies.
  • Nonlinear control of a fully actuated robotic hand using high-order sliding mode and feedback linearization controllers. DOI: https://doi.org/10.1371/journal.pone.0333512

Research Impact

Current bibliometric indicators include three indexed publications, five scholarly citations, and an h-index of one. Although representing an emerging research portfolio, these outputs demonstrate measurable academic engagement. The emphasis on intelligent robotic control establishes a foundation for future investigations with broader scientific and technological influence. [1]

Award Suitability

The documented research aligns with evaluation themes commonly associated with technology-oriented academic awards, particularly innovation, robotics, intelligent control, and engineering advancement. Published contributions addressing adaptive control and robotic system optimization provide objective evidence supporting recognition through the Technology Scientists Awards evaluation framework. [3]

Conclusion

Asra Sarwat has established an emerging scholarly presence within robotics through research focused on adaptive intelligent control and nonlinear robotic systems. The available publications, citation record, and engineering contributions collectively demonstrate continued academic development while supporting future research excellence and professional recognition within the international robotics community. [2]

References

  1. Elsevier. (n.d.). Scopus author details: Asra Sarwat (Author ID: 58956274200). Scopus.
    https://www.scopus.com/pages/authors/58956274200
  2. Sarwat, A. (2026). Adaptive backstepping control with fuzzy logic for real-time motion control of a multi-DoF articulated systems. Robotics and Autonomous Systems.
    https://www.sciencedirect.com/science/article/abs/pii/S0921889026003180?via%3Dihub
  3. Sarwat, A. (2025). Adaptive Backstepping Control with Fuzzy Logic for Real-Time Motion Control of a Multi-DoF Articulated Systems. SSRN Electronic Journal.
    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6389541
  4. Sarwat, A. (2025). Nonlinear control of a fully actuated robotic hand using high-order sliding mode and feedback linearization controllers. PLOS ONE.
    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333512

Yagna Jadeja | Robotics | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

                    Yagna Jadeja
Affiliation PyCRobo Ltd
Country United Kingdom
Scopus ID 57211810459
Documents 6
Citations 28
h-index 2
Subject Area Robotics
Event Technology Scientists Awards
ORCID 0000-0003-4790-3592

Yagna Jadeja is affiliated with PyCRobo Ltd, United Kingdom, and has contributed to robotics research focusing on imitation learning, healthcare robotics, computer-aided robotic design, and intelligent learning systems. This article summarizes the academic profile, research contributions, publication record, research impact, and suitability for the Innovative Research Award based on publicly available scholarly information.[1]

Abstract

Yagna Jadeja has developed research centered on robotics, imitation learning, healthcare assistance, and intelligent robotic systems. The published studies demonstrate practical applications of artificial intelligence for autonomous learning, active image labeling, and computer-aided robotic design. These contributions collectively advance adaptive robotic technologies while supporting efficient human–robot interaction, healthcare automation, and machine learning methodologies. The available publication record, citation metrics, and scholarly visibility indicate sustained engagement with robotics research and justify recognition through the Innovative Research Award for emerging scientific contributions within technology and engineering disciplines.[1][2][3]

Keywords

Robotics, Imitation Learning, Artificial Intelligence, Healthcare Robotics, Machine Learning, Active Learning, Computer-Aided Design, Human–Robot Interaction, Autonomous Systems, Intelligent Robotics.

Introduction

The research portfolio emphasizes robotics supported by imitation learning, artificial intelligence, and intelligent automation. The published investigations address practical healthcare assistance, robotic system design, and efficient data labeling strategies, demonstrating interdisciplinary integration between engineering and machine learning while contributing to the advancement of adaptive robotic technologies for real-world applications.[1][2]

Research Profile

Yagna Jadeja’s scholarly profile reflects research activity in robotics with emphasis on imitation learning, healthcare automation, intelligent perception, and computer-aided robotic development. Indexed publications and measurable citation performance indicate consistent participation in internationally recognized research while maintaining a focused contribution to emerging intelligent robotic technologies.[1]

Research Contributions

The research introduces self-learning robotic systems using deep imitation learning, investigates active learning approaches for reducing image-labeling requirements, and explores computer-aided robotic design methodologies. Together these studies improve autonomous decision-making, learning efficiency, and practical deployment of intelligent robotic systems across healthcare and engineering environments.[1][2][3]

Publications

The publication record includes studies addressing healthcare robotics through deep imitation learning, active learning strategies for image labeling optimization, and computer-aided robotic design. These peer-reviewed publications collectively demonstrate interdisciplinary expertise linking robotics, artificial intelligence, computer vision, and intelligent automation within applied engineering research.[1][2][3]

Research Impact

The documented publications, citations, and Scopus indexing demonstrate scholarly visibility within robotics research. Contributions support ongoing developments in autonomous learning, healthcare assistance, and intelligent engineering while providing practical methodologies that may encourage future innovation across academic research and industrial robotic applications.[1]

Award Suitability

The combination of peer-reviewed publications, measurable citation performance, interdisciplinary robotics research, and practical technological innovation provides a balanced foundation supporting consideration for the Innovative Research Award. The work aligns with recognition criteria emphasizing scientific originality, engineering relevance, and contributions toward intelligent robotic systems.[1][3]

Conclusion

Yagna Jadeja has established an emerging research profile focused on robotics and intelligent learning systems through peer-reviewed publications and measurable scholarly metrics. The available evidence demonstrates meaningful academic engagement, technological relevance, and continued contributions supporting innovation in healthcare robotics and autonomous intelligent systems.[1][2][3]

References

  1. Jadeja, Y., et al. (2025). Enhancing Healthcare Assistance with a Self-Learning Robotics System: A Deep Imitation Learning-Based Solution. Electronics, 14(14), 2823.
    https://www.mdpi.com/2079-9292/14/14/2823
  2. Jadeja, Y., et al. (2024). Various Active Learning Strategies Analysis in Image Labeling: Maximizing Performance with Minimum Labeled Data. In Lecture Notes in Computer Science. Springer.
    https://link.springer.com/chapter/10.1007/978-3-031-53082-1_15
  3. Jadeja, Y., et al. (2022). Computer Aided Design of Self-Learning Robotic System using Imitation Learning. Advances in Design Engineering. IOS Press.
    https://ebooks.iospress.nl/doi/10.3233/ATDE220564
  4. Elsevier. (n.d.). Scopus Author Details: Yagna Jadeja, Author ID 57211810459. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57211810459
  5. ORCID. (n.d.). ORCID Record: Yagna Jadeja.
    https://orcid.org/0000-0003-4790-3592

Harish Sharma | Robotics | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Harish Sharma
Indian Institute of Information Technology Pune
            Harish Sharma
Affiliation Indian Institute of Information Technology Pune
Country India
Subject Area Robotics
Event Technology Scientists Awards
ORCID 0009-0002-5046-9010

The Innovative Research Award recognizes scholarly engagement in robotics and intelligent systems research. This academic profile presents an overview of the research identity, publication relevance, scholarly contributions, and broader impact associated with Harish Sharma at the Indian Institute of Information Technology Pune within the context of contemporary robotics research and scientific recognition.[1]

Abstract

This article documents the academic recognition associated with the Innovative Research Award and highlights research interests connected to robotics and intelligent autonomous systems. The profile emphasizes contributions toward adaptive robotic planning, dynamic navigation strategies, and publication engagement in contemporary robotics literature. It presents a structured overview of scholarly identity, research outcomes, publication alignment, and the significance of scientific contribution within emerging technology ecosystems while maintaining a neutral academic perspective suitable for scholarly presentation and institutional visibility.[1][2]

Keywords

Robotics, Adaptive Path Planning, Autonomous Systems, Dynamic Navigation, Intelligent Algorithms, Research Recognition, Technology Awards, Scholarly Publications.

Introduction

Robotics research continues to advance through integration of adaptive planning methods, autonomous control strategies, and intelligent decision frameworks. Academic recognition programs acknowledge contributions that support reproducibility, technical rigor, and practical relevance across evolving robotic environments and multidisciplinary technological applications.[1]

Research Profile

Harish Sharma is presented in association with the Indian Institute of Information Technology Pune and a scholarly profile connected to robotics-oriented research. The profile reflects participation in research dissemination and engagement with contemporary developments in intelligent and autonomous technological systems.[2]

Research Contributions

Research contributions represented in this profile align with robotic navigation and adaptive planning concepts. Emphasis is placed on approaches that improve operational responsiveness in changing environments and support methodological development for autonomous movement, localized decision processes, and intelligent task execution.[1][2]

Publications

The publication record associated with this recognition highlights engagement with peer-reviewed scholarship addressing robot path planning, optimization methods, and adaptive decision architectures. Publications contribute to ongoing discussions concerning efficient robotic behavior under dynamic environmental conditions.[1]

Research Impact

Research impact is evaluated through dissemination, scholarly visibility, and methodological relevance. Work associated with robotics and adaptive systems supports future investigation into autonomous technologies and encourages integration of intelligent computational approaches across scientific and engineering domains.[2]

Award Suitability

Recognition through the Innovative Research Award aligns with demonstrated scholarly engagement, publication relevance, and contribution to robotics research themes. Evaluation criteria emphasize academic quality, technical significance, and sustained participation in advancing contemporary scientific knowledge.[1]

Conclusion

This academic article presents a structured overview of research recognition and scholarly positioning within robotics. The profile emphasizes publication relevance, contribution orientation, and alignment with broader scientific objectives that support innovation and continued advancement in intelligent robotic systems.[1][2]

References

  1. Transformer-enhanced deep Q-Learning for adaptive robot path planning in dynamic environments. (2026). Cluster Computing.
    https://doi.org/10.1007/s10586-026-06072-2
  2. Dynamic multi-robot coverage framework via A*-optimized region patrolling and localized re-planning. (2026). International Journal of Advanced Robotic Systems.
    https://journals.sagepub.com/doi/full/10.1177/17298806261429541
  3. ORCID. (n.d.). Researcher identifier profile.
    https://orcid.org/0009-0002-5046-9010

Zeng Xiangjin | Robot | Best Academic Researcher Award

Published on by Technology Scientists

Prof. Dr. Zeng Xiangjin | Robot | Best Academic Researcher Award

Professor | Wuhan Institute of Technology | China

Prof. Dr. Xiangjin Zeng is a researcher at the Wuhan Institute of Technology specializing in computer vision, deep learning, and intelligent image processing. His work focuses on advanced techniques for super-resolution, object detection, infrared imaging, and image captioning, integrating attention mechanisms and modern CNN–Transformer architectures. He has authored 37 publications with 165 citations and 6 h-index, reflecting growing global recognition. Dr. Zeng has collaborated with over 30 co-authors, contributing to multidisciplinary advancements in multimedia applications and AI-driven visual analysis. His research supports practical innovations in surveillance, smart imaging systems, and human–machine interaction, strengthening the societal impact of next-generation visual technologies.

Citation Metrics (Scopus)
165
150
100
50
0
Citations

165

Documents

37

h-index

6

Citations

Documents

h-index

View Scopus Profile

Top 5 Featured Publications

Xingxing You | Intelligent control | Editorial Board Member

Published on by Technology Scientists

Assist. Prof. Dr. Xingxing You | Intelligent control | Editorial Board Member

Assistant Professor | Sichuan University | China

Assist. Prof. Dr. Xingxing You is a developing researcher affiliated with Sichuan University, China, whose work spans advanced signal processing, intelligent control, and underwater imaging technologies. With 26 scientific publications, h-index 7and over 408 citations, the author demonstrates an emerging yet steadily growing influence in these fields. His research contributions include multi-level feature fusion strategies for perception-driven underwater image enhancement, advancing the reliability of visual sensing in complex aquatic environments, as well as novel critic-only self-learning optimal control methods for continuum robots operating under unknown disturbances, integrating extended state observer frameworks to elevate robustness and adaptability. These works reflect a broader expertise in machine learning–guided optimization, sensor fusion, and nonlinear dynamical systems, addressing real-world problems where conventional modeling is insufficient. Collaboration is a key dimension of his academic trajectory, with 55 co-authors across disciplines, indicating strong engagement within interdisciplinary research networks and an ability to participate effectively in multi-institutional scientific efforts. His research outcomes demonstrate relevance not only to academic communities working on robotics, automation, and digital signal processing, but also to domains such as marine engineering, environmental monitoring, and intelligent manufacturing. By focusing on interpretable enhancements, computational efficiency, and real-time control, his contributions help bridge theoretical advances and applied technological innovation. Overall, Xingxing You’s scholarly record showcases growing expertise, collaborative capacity, and a commitment to addressing technically challenging problems with practical societal implications.

Profiles: Scopus | ORCID

Featured Publications

1. Perception-driven underwater image enhancement via multi-level feature fusion. (2026). Digital Signal Processing: A Review Journal.

2. Critic-only based self learning optimal control for continuum robots with unknown disturbances via extended state observer. (2025). Nonlinear Dynamics.

Assist. Prof. Dr. Xingxing You’s work advances intelligent sensing and robust control systems, enabling more reliable robotic and imaging technologies in uncertain environments. His research contributes to global innovation by strengthening the scientific foundation for autonomous systems and enhancing their applications in marine exploration, environmental protection, and advanced robotics.

Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

Published on by Technology Scientists

Prof. Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

Associate Dean | Shenyang Aerospace University | China

Xiao Liang is a Professor at Shenyang Aerospace University and Associate Director of the Key Laboratory of Liaoning Province. He earned his B.S. in Automation from Northeastern University, followed by an M.S. in Control Theory and Control Engineering. He received his Ph.D. in Navigation, Guidance, and Control from Beihang University. With over 30 academic papers published in leading journals and five authorized patents, Liang has significantly advanced unmanned aerial and ground vehicle (UAV/UGV) research. His international engagement includes academic exchanges at Swansea University, UK. He has led numerous prestigious projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation, and provincial foundations. His research integrates intelligent decision-making, mission planning, and heterogeneous multi-agent collaboration. Liang’s work bridges theory and practice, addressing challenges in robotics, aerospace, and autonomous systems.

Professional Profiles

ORCID | Scopus

Education 

Xiao Liang’s academic journey demonstrates consistent excellence and commitment to automation and control engineering. He completed his undergraduate studies in Automation at Northeastern University, where he distinguished himself by being recommended for postgraduate study without entrance examinations. He pursued a Master’s degree in Control Theory and Control Engineering, which he earned. His doctoral studies at Beihang University culminated in a Ph.D. in Navigation, Guidance, and Control, specializing in autonomous systems and flight control technologies. This educational trajectory provided Liang with a robust foundation in aircraft design, control theory, and computational modeling. His exposure to both theoretical and applied dimensions of control engineering has enabled him to integrate multidisciplinary perspectives into his research. An academic exchange visit to Swansea University further enriched his international outlook. His education laid the groundwork for his leadership in UAV/UGV cooperative systems and intelligent autonomous decision-making.

Experience 

Xiao Liang is a Professor at Shenyang Aerospace University’s School of Automation, where he supervises master’s students and directs cutting-edge research. He also serves as Associate Director of the Key Laboratory of Liaoning Province. His research leadership includes hosting major projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation of China, and multiple provincial agencies. Liang’s projects span UAV/UGV path planning, dynamic tracking, collaborative sensing, and obstacle avoidance, with applications in rescue missions and aerospace systems. He has published widely in journals such as Intelligent Service Robotics, Robotics and Autonomous Systems, and Aerospace Science and Technology. Beyond academia, his work includes five authorized patents on UAV control systems and path planning methods, underscoring his role in translating research into technological innovations. Liang’s international experience, including research exchange at Swansea University, has further shaped his multidisciplinary approach to intelligent decision-making and multi-agent collaboration.

Research Focus 

Xiao Liang’s research lies at the intersection of control theory, artificial intelligence, and aerospace systems. His primary focus is the design and optimization of heterogeneous multi-agent systems, particularly UAV/UGV collaboration in dynamic and complex environments. His contributions span navigation, guidance, and control (GNC), pursuit-evasion strategies, multi-agent decision-making, and robust target-tracking algorithms. Liang has introduced innovative approaches such as visual SLAM in dynamic environments, semantic octree mapping, and adaptive path planning methods under uncertain conditions. His work also explores mission planning under adversarial and rescue scenarios, emphasizing robustness and fault tolerance. Liang integrates hardware-software design into UAV/UGV systems, ensuring practical applicability. His pioneering contributions extend beyond aerospace into areas such as smart shopping systems for unmanned supermarkets, reflecting the adaptability of his methods. With a balance of theoretical advancement and applied innovation, his research continues to push the boundaries of intelligent multi-agent control and autonomous robotics.

Awards and Honors 

Xiao Liang has been recognized with multiple prestigious awards for his scientific and technological contributions. He received the Science and Technology Award from the Chinese Society of Astronautics, highlighting his impact in aerospace innovation. His mentorship and leadership have also led student teams to achieve top recognition: Second Prize in the 16th “Challenge Cup” National Undergraduate Academic Science and Technology Works, and First Prize in both the 13th and 14th China Graduate Electronic Design Competitions. He was selected for the Liaoning BaiQianWan Talents Program and recognized among the High-level Talents of Shenyang in the same year. His earlier contributions earned the Progress in Science and Technology Award from both Liaoning Province and Shenyang City. Collectively, these honors reflect Liang’s sustained excellence, leadership, and innovation in aerospace engineering, intelligent systems, and multi-agent control research, positioning him as a leading scientist of his generation.

Publication Top Notes

Title: Design and Development of Full Self-Service Smart Shopping System for Unmanned Supermarket
Year: 2025

Title: STSLAM: Robust Visual SLAM in Dynamic Scenes via Image Segmentation and Instance Tracking
Year: 2025

Title: Real-Time Semantic Octree Mapping under Aerial-Ground Cooperative System
Year: 2025

Title: Collaborative Pursuit-Evasion Game of Multi-UAVs Based on Apollonius Circle in the Environment with Obstacle
Year: 2023

Title: Design and Development of Ground Station for UAV/UGV Heterogeneous Collaborative System
Year: 2021

Title: Fault-Tolerant Control for the Multi-Quadrotors Cooperative Transportation under Suspension Failures
Year: 2021

Title: Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
Year: 2021

Title: Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment
Year: 2020

Title: Moving Target Tracking Method for Unmanned Aerial Vehicle/Unmanned Ground Vehicle Heterogeneous System Based on AprilTags
Year: 2020

Title: Moving Target Tracking of UAV/UGV Heterogeneous System Based on Quick Response Code
Year: 2019

Title: Real-Time Moving Target Tracking Algorithm of UAV/UGV Heterogeneous Collaborative System in Complex Background
Year: 2019

Title: A Geometrical Path Planning Method for Unmanned Aerial Vehicle in 2D/3D Complex Environment
Year: 2018

Conclusion

Xiao Liang demonstrates a strong combination of scholarly excellence, technological innovation, and leadership in the field of unmanned systems and intelligent robotics. His contributions to UAV/UGV heterogeneous collaboration, robust control, and path planning strategies position him as a highly competitive candidate for the Research for Outstanding Scientist Award. With further efforts toward global collaboration and broader application of his research outcomes, his profile will continue to strengthen and align well with the award’s standards of excellence.