Papers
18
Total Citations
142
H-Index
8
About
Xiang Yin is a prominent researcher specializing in robot path planning, formal methods, and security-aware control synthesis, with a particular focus on applying linear temporal logic (LTL) and Petri net frameworks to autonomous systems. His work addresses some of the most pressing challenges in multi-robot coordination, including cyclic task optimization, failure robustness, and operation in partially-known environments — areas reflected in his highly cited 2023 paper on multi-robot path planning using Petri nets (21 citations). A defining thread across Yin's research is the integration of security constraints into planning and control. His 2020 work on secure-by-construction optimal path planning (19 citations) pioneered methods to protect robot behavior from passive eavesdroppers, a theme he has extended to stochastic systems, multi-robot teams, and reinforcement learning settings. His neural network-guided planning work (NNgTL) further demonstrates his interest in scaling formal methods to continuous, real-world environments. With contributions spanning LTL specification, Markov decision processes, signal temporal logic, and integer linear programming, Yin's research bridges theoretical rigor and practical robotics. His body of work has accumulated over 110 citations, establishing him as an influential voice in secure, specification-driven autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1Optimal multi-robot path planning for cyclic tasks using Petri nets21 citations · 2023
- 2Secure-by-Construction Optimal Path Planning for Linear Temporal Logic Tasks19 citations · 2020
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