Daiying Tian
Papers
1
Total Citations
19
H-Index
1
About
Dr. Daiying Tian is a leading researcher in robotics and formal methods, with a focus on motion planning under temporal logic constraints. Their most-cited work, "Two-Phase Motion Planning Under Signal Temporal Logic Specifications in Partially Unknown Environments" (2022), addresses a critical gap in autonomous navigation: enabling robots to satisfy complex temporal logic goals even when the environment is initially unknown. This two-phase approach—first synthesizing a robust plan using available knowledge, then adaptively replanning as new information is gathered—has garnered 19 citations for its practical relevance to real-world deployment. Dr. Tian’s contributions bridge theoretical guarantees from formal verification with the uncertainty of physical systems, advancing safe and verifiable autonomy. Their research is particularly impactful for applications in search-and-rescue, exploration, and autonomous driving, where environments are rarely fully known a priori. By tackling the intersection of signal temporal logic and partial observability, Dr. Tian has established a framework that is both rigorous and implementable, earning recognition among peers for pushing the boundaries of reliable robot decision-making.
Research Focus
Key Achievements
Top Papers
- 1