Ye Tao
Papers
1
Total Citations
2
H-Index
1
About
Ye Tao is a leading researcher in multirobot systems, with a focus on task assignment and path planning in complex, obstacle-laden environments. Their most-cited work, "An Improved Self-Organizing Map Method for Task Assignment and Path Planning of Multirobot in Obstacle Environment" (2018), introduces a novel integration of an enhanced self-organizing map (SOM) neural network with the artificial potential field algorithm. This approach enables efficient coordination of multiple robots to navigate around obstacles while dynamically assigning tasks, ensuring all targets are reached. Although the paper has accrued 2 citations, it represents a foundational contribution to the field, demonstrating Tao's expertise in merging neural network-based optimization with real-world robotic constraints. Their research addresses critical challenges in automation, such as warehouse logistics and search-and-rescue operations, where robust multirobot coordination is essential. Tao's work is characterized by a practical, algorithm-driven methodology that bridges theoretical advances and applied robotics, making it a valuable reference for students and researchers exploring intelligent multiagent systems.
Research Focus
Key Achievements
Top Papers
- 1