Papers
5
Total Citations
180
H-Index
5
About
Wenli Du is a leading researcher in autonomous systems, with a focus on perception, navigation, and decision-making in the era of machine learning. Their work bridges computer vision and reinforcement learning to enable robots to understand and navigate complex environments as effectively as humans. Du’s highly cited survey, “Perception and Navigation in Autonomous Systems in the Era of Learning” (2022, 133 citations), provides a comprehensive roadmap for integrating deep learning into self-state estimation, environmental perception, and autonomous navigation. They have also made notable contributions to infrared dim small target detection with the RLPGB-Net (2023), which fuses reinforcement learning with pyramid-feature attention for aerial target detection. Du’s earlier work includes OnionNet (2020), an unsupervised framework for single-view depth prediction and camera pose estimation from unlabeled video, and a foundational overview of perception and decision-making in autonomous systems (2020, 16 citations). Their recent research extends into multi-agent systems, proposing finite-time sliding mode control under fuzzy topologies (2025). With a growing citation record and a clear trajectory from foundational surveys to cutting-edge control and detection methods, Du is shaping the future of intelligent, learning-driven autonomy.
Research Focus
Key Achievements
Top Papers
- 1Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey133 citations · 2022
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