Xiaoran Yan
Papers
1
Total Citations
19
H-Index
1
About
Xiaoran Yan is a leading researcher in mobile robotics and intelligent control systems, with a primary focus on real-time path planning in unknown dynamic environments. Their most notable contribution is the development of the BOAE-DDPG (Bidirectional Obstacle Avoidance Enhancement-Deep Deterministic Policy Gradient) algorithm, a novel deep reinforcement learning approach that enables mobile robots to navigate safely and efficiently through complex, unpredictable settings. This work, published in 2024, has already garnered 19 citations, reflecting its immediate impact on the field. By training agents through direct environmental interaction, Yan’s algorithm addresses a critical challenge in robotics: the need for adaptive, real-time decision-making without pre-mapped routes. Their research bridges the gap between theoretical reinforcement learning and practical robotic applications, offering a scalable solution for autonomous navigation in dynamic spaces. Yan’s work is particularly valuable for students and researchers exploring the intersection of artificial intelligence and robotics, as it provides a clear framework for applying DDPG-based methods to real-world problems. With a growing citation record and a focus on cutting-edge AI-driven control, Yan is establishing themselves as a key innovator in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1