Papers
10
Total Citations
136
H-Index
5
About
Jin Dai's research lies at the intersection of formal methods, multi-agent systems, and robotics, with a core focus on synthesizing provably correct motion plans for networked robots. His most impactful work addresses the critical coupling between robot motion and communication quality, developing frameworks that ensure multi-agent teams satisfy complex temporal logic specifications while maintaining reliable inter-agent connectivity. His 2017 paper on communication-aware motion planning from Signal Temporal Logic specifications (50 citations) established a foundational mathematical framework for this problem, followed by a comprehensive journal article in 2020 (37 citations) that formalized distributed control under communication constraints. Dai has also made significant contributions to cooperative multi-agent control synthesis, pioneering combined top-down and bottom-up design approaches that decompose global team missions into local tasks while respecting connectivity constraints. His work on automatic synthesis of cooperative systems (2014) and learning-based formal synthesis (2016) further advanced the field by integrating machine learning with formal verification techniques. Beyond multi-agent coordination, Dai has explored continuous curvature path planning for car-like robots, investigating transition schemes and existence conditions for smooth, real-time trajectories. His research, supported by multiple NSF grants, has been instrumental in bridging the gap between high-level task specifications and low-level motion control for networked robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Automatic synthesis of cooperative multi-agent systems15 citations · 2014
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- 7On existence conditions of a class of continuous curvature paths2 citations · 2017
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