Papers
54
Total Citations
1,804
H-Index
27
About
Anh Vu Le is a prominent robotics and artificial intelligence researcher whose work centers on coverage path planning, reconfigurable robotics, and autonomous systems. He is perhaps best known for pioneering the application of machine learning — including reinforcement learning and deep reinforcement learning — to the challenging problem of complete coverage path planning (CPP), particularly for self-reconfigurable, Tetromino-inspired robots designed for cleaning, maintenance, painting, and mining applications. His most cited work (185 citations) introduced reinforcement learning to optimize CPP for modular tiling robots, while a closely related study adapting the A* algorithm garnered 163 citations, together establishing him as a leading voice in intelligent robot navigation. Le has further advanced the field by integrating deep reinforcement learning with the Travelling Salesman Problem for complex environments (89 citations) and developing energy-efficient path planning strategies for reconfigurable systems (78 citations). Beyond path planning, his contributions extend to computer vision and debris classification for floor-cleaning robots, table-cleaning systems using deep learning, and autonomous ship hull maintenance. With over 870 cumulative citations across his top works, Le's research has meaningfully shaped how autonomous robots perceive, plan, and adapt in real-world environments.
Research Focus
Key Achievements
Top Papers
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- 8Table Cleaning Task by Human Support Robot Using Deep Learning Technique58 citations · 2020
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