Fumeng Ye
Papers
2
Total Citations
4
H-Index
2
About
Fumeng Ye’s research advances the frontiers of autonomous underwater robotics, with a focus on intelligent navigation and adaptive control in complex aquatic environments. Ye’s most notable contribution is a large-scale path planning algorithm for underwater robots, leveraging deep reinforcement learning to enhance both the efficiency and accuracy of long-distance autonomous navigation. This work, published in 2024, has already garnered early citations, signaling its potential impact on the field of marine robotics. Ye also explored the integration of IoT and fuzzy neural network algorithms for adaptive control of underwater tunnel monitoring robots, though that paper was subsequently retracted due to compromised peer review. Despite this setback, Ye’s core research demonstrates a commitment to solving real-world challenges in underwater exploration and infrastructure inspection. With over 2 citations on the most-cited paper, Ye’s work is gaining recognition among researchers developing intelligent, data-driven solutions for autonomous underwater vehicles. Ye’s contributions are particularly relevant for students and engineers interested in the intersection of reinforcement learning, robotics, and environmental monitoring.
Research Focus
Key Achievements
Top Papers
- 1
- 2