Yingwen Tan
Papers
2
Total Citations
13
H-Index
2
About
Yingwen Tan is a robotics researcher whose work focuses on advancing autonomous navigation through innovative path-planning algorithms. Tan’s primary contributions lie in multi-objective and multi-directional path planning for mobile robots, addressing critical challenges in applications such as mobility-as-a-service, industrial inspection, and electric vehicle routing. Their most cited work, the “Informable Multi-Objective and Multi-Directional RRT* System” (2022, 8 citations), introduces an anytime iterative framework that simultaneously optimizes for multiple conflicting objectives—like travel time, energy efficiency, and safety—while enabling robots to plan routes toward multiple destinations. A refined version of this system (2023, 5 citations) further enhances scalability and real-time adaptability. By extending the classic RRT* algorithm into a multi-objective, multi-directional context, Tan has provided a practical tool for robots operating in complex, dynamic environments. Their research bridges theoretical planning with real-world deployment, offering a flexible foundation for future autonomous systems. With growing citation impact, Yingwen Tan is establishing themselves as a thoughtful contributor to the field of intelligent robotics and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2