Papers
1
Total Citations
7
H-Index
1
About
Xuefeng Yan is a leading researcher in robotics and autonomous systems, with a primary focus on motion planning and its computational foundations. His work critically addresses the challenge of balancing algorithmic completeness with real-time efficiency, a core tension in modern robotics. Yan is best known for his comprehensive overview and comparative analysis of traditional motion planning methods based on Rapidly Exploring Random Trees (RRTs), a seminal contribution that has already garnered 7 citations since its 2025 publication. This work systematically evaluates the trade-offs between complete algorithms—which guarantee path solutions but are computationally prohibitive—and sampling-based approaches like RRTs, which offer practical speed for real-world applications. By clarifying these distinctions, Yan has provided a vital resource for researchers and engineers seeking to deploy autonomous robots in dynamic environments. His research continues to influence the development of more efficient, scalable motion planning algorithms, with implications for everything from industrial automation to autonomous driving. Yan’s contributions are essential reading for anyone navigating the intersection of theoretical guarantees and practical performance in robotics.
Research Focus
Key Achievements
Top Papers
- 1