Yangji Fan
Papers
1
Total Citations
4
H-Index
1
About
Yangji Fan is a researcher focused on advancing autonomous navigation for indoor mobile robots, with a particular emphasis on map processing and path planning. Their most-cited work, "Research on Map Partitioning and Preprocessing Algorithms for Global Path Planning" (2023), introduces a novel indoor partitioning algorithm that leverages SLAM technology to enhance how robots interpret and navigate complex indoor environments. This contribution addresses a critical bottleneck in robotics—enabling efficient, real-time map segmentation without reliance on computationally heavy neural networks. While their citation count is currently modest, the work represents a practical, algorithm-driven approach to improving robot autonomy. Fan’s research sits at the intersection of robotics, spatial intelligence, and algorithmic optimization, offering a scalable solution for tasks like warehouse logistics or service robotics. Their focus on preprocessing and partitioning algorithms underscores a commitment to foundational problems that underpin more advanced navigation systems, making their work a valuable reference for students and engineers seeking efficient, lightweight alternatives to deep learning methods in path planning.
Research Focus
Key Achievements
Top Papers
- 1