Yumin Peng
Papers
2
Total Citations
5
H-Index
2
About
Yumin Peng is a researcher specializing in autonomous navigation, multi-sensor data fusion, and deep reinforcement learning for robotic systems. Their work addresses critical challenges in unstructured and large-scale environments, particularly for underwater robots. Peng’s major contributions include developing a trajectory calibration technology based on multi-data fusion, which enhances navigation accuracy in complex, unstructured terrains—a foundational approach for real-world deployment. Additionally, they proposed a large-scale path planning algorithm for underwater robots using deep reinforcement learning, designed to improve both efficiency and precision over vast areas. This algorithm, published in 2024, has already garnered attention for its potential to transform autonomous underwater operations. With early citations totaling 5 across their most-cited papers, Peng’s research is gaining traction in the robotics community, highlighting its relevance to advancing autonomous systems in challenging domains. Their work stands out for integrating cutting-edge AI techniques with practical robotic navigation, promising significant impacts on marine exploration, environmental monitoring, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2