Yaohan Tang
Papers
1
Total Citations
6
H-Index
1
About
Yaohan Tang is a robotics researcher whose work lies at the intersection of autonomous navigation, bioinspired algorithms, and human–robot interaction. Their most-cited paper, “Complete Coverage Path Planning Based on Bioinspired Neural Network and Pedestrian Location Prediction” (2018, 6 citations), addresses a critical limitation in mobile robot coverage tasks: the “collision problem” caused by passive obstacle avoidance in dynamic environments. By integrating bioinspired neural networks with pedestrian location prediction, Tang proposed a proactive strategy that enables robots to anticipate and avoid moving obstacles in real time, significantly improving safety and efficiency in crowded or unpredictable settings. This contribution is particularly relevant for applications in service robotics, warehouse automation, and assistive technologies. Tang’s work demonstrates a thoughtful synthesis of computational neuroscience and practical path planning, offering a more adaptive and intelligent approach to complete coverage tasks. With a growing citation footprint, their research is gaining recognition among scholars working on autonomous navigation and human-aware robotics. Tang continues to push the boundaries of how robots perceive and move through human-centered environments.
Research Focus
Key Achievements
Top Papers
- 1