Jingtian Yan
Papers
2
Total Citations
17
H-Index
2
About
Jingtian Yan is an emerging researcher specializing in multi-agent systems and autonomous robot motion planning, with a particular focus on developing computationally efficient algorithms for coordinating multiple robots in complex, real-world environments. Their work addresses the challenging intersection of multi-agent path-finding and kinodynamic motion planning — bridging the gap between theoretical graph-based search methods and physically realizable robot trajectories. Yan's most notable contribution, "Multi-Agent Motion Planning With Bézier Curve Optimization Under Kinodynamic Constraints" (2024), has already garnered 15 citations, a strong indicator of early-career impact within the robotics and AI planning communities. This work advances the field by leveraging Bézier curve optimization to generate smooth, collision-free trajectories that respect the physical dynamics of moving agents — a critical requirement for practical deployment in warehouse automation, airport logistics, and traffic management systems. Their follow-up work on differential drive robots further demonstrates a commitment to grounding theoretical planning frameworks in realistic robot models. With applications spanning industrial automation and autonomous vehicle coordination, Yan's research is carving out an important niche at the frontier of scalable, real-world multi-robot systems — making their profile one to watch in the coming years.
Research Focus
Key Achievements
Top Papers
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- 2