Sheng‐Wei Lee
Papers
1
Total Citations
2
H-Index
1
About
Sheng-Wei Lee is a robotics researcher whose work focuses on autonomous navigation systems, particularly path planning for unmanned vehicles in real-world environments. His most cited paper, "A Path Planning Algorithm based on Leading Rapidly-exploring Random Trees" (2019), addresses a critical challenge in mobile robotics: efficiently generating collision-free trajectories for applications like factory automated guided vehicles and delivery robots. The algorithm improves upon standard RRT methods by introducing a "leading" mechanism that guides tree expansion toward the goal, reducing computational overhead while maintaining safety. Though his citation count currently stands at 2, the work is foundational for researchers tackling the intersection of mapping, localization, and motion planning—three pillars of autonomous navigation that Lee explicitly connects in his research. His contributions are particularly relevant as delivery drones and warehouse robots become ubiquitous, highlighting the practical importance of efficient path planning in constrained, dynamic environments. Lee's approach offers a scalable solution for systems requiring real-time navigation without exhaustive environmental pre-mapping, making his work a valuable reference for students and engineers developing next-generation autonomous vehicles.
Research Focus
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Top Papers
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