Kyongsik Yun
Papers
2
Total Citations
5
H-Index
2
About
Kyongsik Yun is a researcher at the forefront of autonomous systems, with a primary focus on trajectory optimization for space exploration and robotics. His key contributions lie in developing methods to accelerate nonlinear optimization solvers, a critical bottleneck for real-time autonomous operations in challenging, unstructured environments. Yun’s most cited work, "Constraint-Informed Learning for Warm-Starting Trajectory Optimization" (2025), tackles this challenge by using machine learning to provide high-quality initial guesses—or "warm starts"—for trajectory solvers, dramatically reducing computation time without sacrificing solution accuracy. This approach is vital for future spacecraft and surface rovers that must make split-second decisions during landing, navigation, or sample collection. With a growing citation count reflecting the timeliness of his research, Yun is helping bridge the gap between theoretical optimization and practical, onboard autonomy. His work represents a significant step toward making autonomous exploration of the Moon, Mars, and beyond both safer and more efficient.
Research Focus
Key Achievements
Top Papers
- 1Constraint-Informed Learning for Warm-Starting Trajectory Optimization3 citations · 2025
- 2Constraint-Informed Learning for Warm Starting Trajectory Optimization2 citations · 2023