Jingtao Tang
Papers
1
Total Citations
2
H-Index
1
About
Jingtao Tang is an emerging researcher specializing in multi-robot systems and autonomous navigation, with a particular focus on coverage path planning algorithms. Their most notable work introduces TMSTC*, a sophisticated turn-minimizing algorithm designed for multi-robot coverage path planning, published in 2022. This contribution addresses a critical challenge in robotics: efficiently coordinating multiple robots to cover large-scale environments while minimizing energy-consuming turns, which directly impacts battery life and operational efficiency in real-world deployments. Tang's research sits at the intersection of algorithmic design and practical robotics applications, leveraging spanning tree coverage frameworks to optimize robot coordination strategies. By extending classical single-robot coverage approaches to multi-robot systems, their work tackles scalability challenges that are essential for industrial applications such as autonomous cleaning, agricultural surveying, and search-and-rescue operations. While still early in their research career — with TMSTC* accumulating 2 citations since publication — Tang's work represents a meaningful technical advancement in a competitive and rapidly evolving field. Students and researchers working on swarm robotics, autonomous systems, or path planning optimization will find Tang's algorithmic contributions a valuable foundation for understanding cooperative multi-robot coverage strategies.
Research Focus
Key Achievements
Top Papers
- 1