Innovative Research Award
Xingkai Yu — North China Electric Power University, China
Xingkai Yu is a researcher affiliated with North China Electric Power University whose documented research includes robotic swarm coordination, battery state-of-charge estimation, control systems, and intelligent electrical-system analysis. His publication record includes work addressing distributed robotic coverage and advanced estimation methods for lithium batteries. [1]
Abstract
The Innovative Research Award recognizes research activity demonstrating methodological development, interdisciplinary relevance, and potential practical value. Xingkai Yu’s documented work spans mobile robotic swarm coordination, battery state estimation, control, and intelligent electrical-system analysis, providing evidence of research engagement across robotics and automation-related engineering domains. [1] [2]
Keywords
Keywords: Mobile Robotics Systems; Robotic Swarms; Anti-Flocking; Dynamic Coverage; Battery State Estimation; Kalman Filtering; Intelligent Control; Automation; Fault Detection.
Introduction
Xingkai Yu’s research profile reflects interdisciplinary work connecting robotics, automation, control, and intelligent energy systems. His research includes distributed robotic-swarm coordination for dynamic coverage and algorithmic approaches to lithium-battery state estimation. These studies address autonomous decision-making, estimation, and control problems relevant to modern cyber-physical engineering systems. [2]
Research Profile
The available publication evidence indicates a research profile centered on intelligent systems and engineering automation. Relevant areas include mobile robotic swarms, distributed coordination, nonlinear estimation, battery-management algorithms, and electrical fault analysis. This combination demonstrates an applied research orientation involving sensing, computation, control, and autonomous system decision processes. [3]
Research Contributions
A principal contribution is research on dynamic coverage using self-organized robotic swarms and an anti-flocking mechanism, addressing distributed coordination in mobile robots. Another contribution concerns lithium-battery state-of-charge estimation through an improved firefly-algorithm-optimized dual extended Kalman filter. Together, these studies demonstrate algorithmic work in autonomous coordination and intelligent estimation. [1]
Publications
The selected publications illustrate the breadth of the research record. The robotic-swarm study develops anti-flocking models for dynamic coverage, while the battery study addresses state-of-charge estimation using an optimized dual extended Kalman filtering framework. A related series AC arc-fault study using singular-spectrum statistical features further demonstrates the wider application of signal-processing and intelligent-analysis methods.
- Sun, X., Duan, X., Dai, J., Qu, Z., Yu, X., & Jin, G. (2026). Dynamic coverage of self-organized robotic swarm via anti-flocking mechanism. Aerospace Science and Technology. https://doi.org/10.1016/j.ast.2026.113061 [1]
- Du, Y., Yu, X., Jin, G., & Li, J. (2026). Lithium battery state of charge estimation based on improved firefly algorithm optimized dual extended Kalman filter. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2026.3675113 [2]
- Xiong, D., Yang, S., Xue, Y., Zhang, P., Song, R., & Song, J. (2025). Fast identification of series arc faults based on singular spectrum statistical features. Electronics, 14(16), 3337. https://doi.org/10.3390/electronics14163337 [3]
Research Impact
The research has potential relevance to autonomous robotics, intelligent transportation and energy-management technologies. Distributed swarm coverage addresses coordination in multi-robot environments, while battery state estimation supports reliable monitoring within battery-management systems. Signal-analysis research on arc faults illustrates the broader applicability of computational methods to intelligent electrical safety and monitoring. [2] [3]
Award Suitability
The documented research provides a reasonable basis for consideration for an Innovative Research Award because it includes peer-reviewed work addressing algorithmic innovation in robotic coordination and intelligent estimation. The combination of mobile robotics, autonomous swarm behavior, control, and computational signal processing aligns with research themes commonly associated with robotics and automation. [3]
Conclusion
Xingkai Yu’s documented research combines robotic swarm coordination, intelligent estimation, and engineering-oriented computational methods. The selected publications demonstrate relevance to autonomous systems and automation through distributed coverage, optimized battery estimation, and signal-based fault analysis. On this evidence, the research profile presents a substantive basis for consideration under an innovation-focused robotics award. [1] [2] [3]
External Links
References
- Dynamic Coverage of Self-organized Robotic Swarm Via Anti-flocking Mechanism.
https://www.researchgate.net/publication/408338309_Dynamic_Coverage_of_Self-organized_Robotic_Swarm_Via_Anti-flocking_Mechanism
- Lithium Battery State of Charge Estimation Based on Improved Firefly Algorithm Optimized Dual Extended Kalman Filter.
https://www.researchgate.net/publication/403377066_Lithium_Battery_State_of_Charge_Estimation_Based_on_Improved_Firefly_Algorithm_Optimized_Dual_Extended_Kalman_Filter
- A Series AC Arc Fault Detection Method Based on Singular Spectrum Analysis
/i>(16), 3337.
https://www.mdpi.com/2079-9292/9/9/1367
- Elsevier. (n.d.). Scopus author details: Xingkai Yu, Author ID 57201419537. Scopus.
https://www.scopus.com/pages/authors/57201419537