Chee Sheng Tan
Papers
2
Total Citations
332
H-Index
2
About
Chee Sheng Tan is a leading researcher in robotics and autonomous systems, with a primary focus on coverage path planning and reinforcement learning for mobile and industrial robots. His most influential work, a comprehensive 2021 review of coverage path planning using classical and heuristic algorithms, has garnered over 300 citations, establishing it as a foundational resource for optimizing robot efficiency in tasks like cleaning, inspection, and manufacturing. Tan’s review critically addresses the bottleneck of limited battery life and unoptimized planning, offering a taxonomy of algorithms that balance speed and accuracy. More recently, he introduced an expected-mean gamma-incremental reinforcement learning algorithm for robot path planning, a novel approach that enhances adaptive decision-making in dynamic environments. This work, already cited 19 times since 2024, demonstrates his commitment to advancing real-time, energy-aware navigation. Tan’s contributions are pivotal for students and researchers seeking to understand the intersection of heuristic optimization and machine learning in robotics, providing both a broad survey and cutting-edge algorithmic innovations that push the boundaries of autonomous coverage.
Research Focus
Key Achievements
Top Papers
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- 2