Papers
7
Total Citations
72
H-Index
4
About
Cui Ni is a researcher specializing in mobile robotics, deep reinforcement learning, and simultaneous localization and mapping (SLAM), with a particular focus on intelligent path planning and autonomous navigation systems. Her work addresses critical challenges in enabling mobile robots to operate efficiently in complex, real-world environments. Ni's most significant contributions center on enhancing Deep Deterministic Policy Gradient (DDPG) algorithms for robot path planning. Her 2022 paper integrating Long Short-Term Memory networks into DDPG frameworks has garnered 46 citations, demonstrating substantial community impact. By tackling limitations such as slow convergence, low training efficiency, and ineffective experience replay sampling, her research has meaningfully advanced the practical deployment of deep reinforcement learning in robotics. Her 2023 work on multi-dimensional transition priority fusion further refined experience replay mechanisms, improving how robots learn from their environments. Equally notable is Ni's research on loop closure detection within SLAM systems, where she has developed innovative solutions using semantic segmentation and multi-dimensional image feature fusion to reduce false-positive detections in visually ambiguous indoor environments. Collectively, her publications have accumulated over 70 citations, establishing her as a productive contributor to the robotics and autonomous systems research community.
Research Focus
Key Achievements
Top Papers
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- 6Deep Q network algorithm based on sample screening3 citations · 2022
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