Jingwen Ai
Papers
2
Total Citations
6
H-Index
2
About
Jingwen Ai is a robotics researcher whose work focuses on advancing autonomous navigation and maintenance systems for specialized environments, particularly underwater and industrial settings. Ai’s key research areas include simultaneous localization and mapping (SLAM) for underwater robots and the development of maintenance robots for electrical substations. In their most-cited work, “The application of square-root cubature Kalman filter in SLAM for underwater robot” (2017, 4 citations), Ai addressed critical limitations in underwater robot navigation by designing a square-root cubature Kalman filter SLAM algorithm (SRCKF-SLAM). This innovation improved convergence speed, accuracy, and numerical stability compared to traditional extended Kalman filter methods, offering a more reliable solution for autonomous underwater exploration. Ai’s second notable paper, “Development of the Maintenance Robot with Electrification Used in Substation” (2017, 2 citations), tackles the practical challenge of maintaining open-type substation insulators, which are prone to flashover accidents due to environmental pollutants. By proposing a robotic system for electrified maintenance, Ai contributed to safer and more efficient infrastructure upkeep. Though early in their career, Ai’s work demonstrates a clear commitment to solving real-world robotics challenges, with potential for significant impact as these technologies mature.
Research Focus
Key Achievements
Top Papers
- 1
- 2