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

Bhargav Prajwal Pathri | Robotics | Editorial Board Member

Published on by Technology Scientists

Dr. Bhargav Prajwal Pathri | Robotics | Editorial Board Member

Associate Professor | Woxsen University | India

Dr. B. Prajwal is an emerging researcher specializing in swarm intelligence, swarm robotics, and computational optimization, with a growing scholarly footprint reflected in 19 publications, 147 citations, and an h-index of 5. His work focuses on designing scalable, adaptive algorithms—such as particle swarm optimization and rendezvous strategies—to enhance coordination and autonomy in multi-robot systems, exemplified by his 2025 article in the Journal of Field Robotics. With collaborations involving over 45 co-authors, his research bridges artificial intelligence, robotics engineering, and algorithmic design, contributing to interdisciplinary advancements and practical implementations. Dr. Prajwal’s studies support the development of resilient, low-cost autonomous systems with applications in environmental monitoring, disaster response, smart agriculture, and industrial automation. Through a combination of analytical rigor and application-oriented inquiry, his work strengthens global innovation in intelligent robotic systems while addressing societal needs for safer, more efficient, and scalable automation technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Mali, H. S., Prajwal, B., Gupta, D., & Kishan, J. (2018). Abrasive flow finishing of FDM printed parts using a sustainable media. Rapid Prototyping Journal, 24(3), 593–606.

Cited by: 69.

2. Sharma, A., Babbar, A., Tian, B., Prajwal, M., Gupta, R., & Singh, R. (2022). Machining of ceramic materials: A state-of-the-art review. International Journal on Interactive Design and Manufacturing, 16(3).

Cited by: 61.

3. Prakash, C., VK, 3., Mistri, A., Uppal, A. S., Babbar, A., Pathri, B. P., Mago, J., … (2021). Investigation of functionally graded adherents on failure of socket joint of FRP composite tubes. Materials, 14(21), 1–13.

Cited by: 27.

4. Unune, D., Aherwar, A., Pathri, B., & Kishan, J. (2014). Statistical and regression analysis of vibration of carbon steel cutting tool for turning of EN24 steel using design of experiments. International Journal of Recent Advances in Mechanical Engineering, 3(3).

Cited by: 18.

5. Pathri, M. K. D., & Prajwal, B. (2013). Numerical analysis of Kevlar-epoxy composite plate subjected to ballistic impact. International Journal of Mechanical Engineering, 41(1), 1117–1122.

Cited by: 16.

Dr. Prajwal’s work advances the future of autonomous multi-robot systems by integrating intelligent optimization with real-world robotics. His research supports scalable, resilient technologies with applications across industry, environmental management, and societal safety, contributing to global innovation in autonomous systems engineering.