Papers
46
Total Citations
927
H-Index
17
About
Shuhuan Wen is a prolific robotics researcher whose work spans autonomous navigation, simultaneous localization and mapping (SLAM), and intelligent control systems. With a career spanning over a decade, Wen has made significant contributions to the intersection of machine learning and robotics, consistently advancing the capabilities of both single and multi-robot systems operating in complex, dynamic environments. Among Wen's most impactful contributions is pioneering the application of deep reinforcement learning to active SLAM and path planning under unknown environments, with his 2020 study accumulating 98 citations and his 2021 multi-robot navigation work earning 84. His earlier research on Elman neural network-based fuzzy adaptive control for mobile robot obstacle avoidance (2011, 70 citations) demonstrated a prescient commitment to intelligent, adaptive robotics long before deep learning became mainstream. More recently, Wen has pushed the frontier of collaborative robotics with edge-assisted multi-robot visual-inertial SLAM (2024, 49 citations), integrating cloud and edge computing to enable real-time global mapping. His work on dynamic scene SLAM and stereo visual-inertial systems further underscores his versatility. Across parallel robot control and humanoid arm planning, Wen's research consistently bridges theoretical rigor with real-world applicability, making him a key figure in modern autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 4Edge-Assisted Multi-Robot Visual-Inertial SLAM With Efficient Communication49 citations · 2024
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- 6Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes47 citations · 2023
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- 9Path Planning of Humanoid Arm Based on Deep Deterministic Policy Gradient30 citations · 2018
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