Yiming Ji
Papers
1
Total Citations
5
H-Index
1
About
Yiming Ji is an emerging researcher whose work sits at the intersection of machine learning and robotics, with a particular focus on intelligent navigation and autonomous systems. Ji's most notable contribution to date is the development of NNPP (Neural Network Path Planning), a learning-based heuristic model designed to accelerate optimal path planning across uneven terrain — a challenging problem with significant implications for autonomous robots operating in real-world, unpredictable environments. Published in 2025, this work has already garnered 5 citations, a promising indicator of early impact within a competitive field. By leveraging neural networks as heuristic guides, Ji's approach addresses a long-standing computational bottleneck in classical path planning algorithms, offering a smarter and faster alternative for traversability analysis in complex landscapes. This research holds particular relevance for applications in search-and-rescue robotics, autonomous vehicles, and planetary exploration. As a researcher at the early stage of what appears to be a productive career, Yiming Ji represents a forward-thinking voice in AI-driven robotics, with foundational work that bridges theoretical machine learning with practical, real-world deployment challenges.
Research Focus
Key Achievements
Top Papers
- 1