Yinglin Li
Papers
4
Total Citations
52
H-Index
3
About
Yinglin Li is a robotics researcher whose work centers on autonomous navigation, human-robot interaction, and safe path planning for mobile robots in challenging, GPS-denied environments. Their most impactful contribution is a dual-layer path planning framework integrated with Pose SLAM, enabling robots to explore unknown spaces without external positioning—a paper that has garnered 28 citations since 2023. Li also pioneered a bidirectional trust-based variable autonomy system, reconciling conflicting human-robot intents during remote operation, which has accumulated 12 citations. In 2025, they introduced RA-RRTV*, a risk-averse sampling-based planner designed to navigate narrow passages under localization uncertainty, earning 10 citations for its novel approach to safety in constrained spaces. Further, Li’s work on human-centered shared autonomy, leveraging suboptimal rationality insights to mitigate over-assistance in teleoperated robots, demonstrates a commitment to improving user experience and reducing control conflicts. With a growing citation record and contributions spanning autonomous exploration, trust-aware control, and risk-sensitive planning, Yinglin Li is establishing a reputation for advancing the reliability and adaptability of mobile robots in real-world, uncertainty-laden settings.
Research Focus
Key Achievements
Top Papers
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