Mengyuan Jin
Papers
2
Total Citations
64
H-Index
2
About
Mengyuan Jin is a researcher at the forefront of intelligent robotics and autonomous navigation, with a primary focus on path planning and visual perception in dynamic environments. Her most impactful work, "Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments" (2022, 62 citations), introduces a novel hybrid approach that combines conflict-based search with the D* Lite algorithm, enabling robots to efficiently navigate and avoid collisions in real-time, uncharted settings—a critical advancement for applications in search-and-rescue and autonomous driving. Jin further extends her expertise into robot vision with "SimCLR-Inception: An Image Representation Learning and Recognition Model for Robot Vision" (2023), which leverages contrastive learning to enhance visual recognition, demonstrating her commitment to integrating deep learning with robotic systems. Her work is notable for bridging theoretical algorithm design with practical deployment challenges, earning recognition for its potential to improve safety and efficiency in autonomous systems. With a growing citation record, Jin’s contributions are shaping the next generation of adaptive, intelligent robots capable of operating in unpredictable real-world conditions.
Research Focus
Key Achievements
Top Papers
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- 2