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About

Yingying Ran is a robotics researcher whose work focuses on advancing the localization, mapping, and scene understanding capabilities of indoor mobile robots and manipulators. Her key research areas include multi-sensor fusion, simultaneous localization and mapping (SLAM), and intelligent calibration for robotic systems. Ran’s most impactful contribution is the development of an adaptive federated Kalman filter (AFKF) for indoor mobile robot positioning, which addresses the inaccuracies of single-sensor systems by dynamically fusing data from multiple sources—a paper that has garnered 50 citations. She has also made significant strides in improving robotic arm precision through a novel kinematic calibration method based on an improved manta ray foraging optimization algorithm, cited 35 times. Her comprehensive review of 2D LiDAR SLAM, published in 2025, synthesizes progress in filter-based and optimization-based approaches, serving as a valuable resource for researchers. Additionally, Ran has explored scene classification using multi-scale convolutional neural networks enhanced by long short-term memory and whale optimization algorithms, pushing the boundaries of how robots perceive and categorize indoor environments. With a growing citation record and a focus on practical, real-world challenges, Yingying Ran’s work is shaping the future of intelligent, autonomous robotics.