Hui Lu
Papers
6
Total Citations
103
H-Index
4
About
Hui Lu is a leading researcher in intelligent robotics, specializing in path planning, multi-robot coordination, and autonomous navigation in unknown environments. Their most significant contribution is the **Experience-Memory Q-Learning (EMQL) algorithm** (2020, 60 citations), which dramatically accelerates robot path planning by leveraging continuously updated shortest-distance memory, solving the classic slow-convergence problem in unknown settings. Lu further advanced the field with a **multi-stage optimization method for multi-robot map building** (2021, 16 citations), enabling accurate global map fusion from individual robot data. Their work on **bidirectional associative learning** (2021, 12 citations) introduced a fast, collision-free path planning approach, while their **cooperative exploration algorithm** (2022, 9 citations) balances efficiency and workload distribution among robot teams. Most recently, Lu tackled communication constraints in multi-robot systems (2025, 4 citations) and developed a **Q-learning-guided memetic algorithm** (2025, 2 citations) that integrates learning with optimization for robust, stable path planning. With over 100 total citations, Lu’s research consistently bridges reinforcement learning and practical robotics, offering scalable solutions for real-world autonomous exploration, disaster response, and industrial automation.
Research Focus
Key Achievements
Top Papers
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