Ming-Song Chen
Papers
3
Total Citations
16
H-Index
3
About
Ming-Song Chen is a rising researcher in robotics and autonomous systems, with a focus on navigation, exploration, and control in challenging, unknown environments. His work bridges path planning, deep reinforcement learning, and robust control theory. Chen’s most cited paper, “E-Planner: An Efficient Path Planner on a Visibility Graph in Unknown Environments” (2024, 8 citations), introduces a novel planner that optimizes obstacle contours and uses prioritized exploration for efficient mobile robot navigation. In 2025, he advanced autonomous exploration with a LiDAR-based deep reinforcement learning method (5 citations), addressing low learning efficiency in tasks like mine exploration and search-and-rescue. His earlier work, “A Generalized Multivariable Adaptive Super-Twisting Control and Observation for Amphibious Robot” (2022, 3 citations), proposes a finite-time attitude control algorithm for stabilizing amphibious robots after state switches. Though early in his career, Chen’s contributions are gaining traction for their practical impact on real-world robotics, particularly in unknown and dynamic settings. His research is valuable for students and engineers interested in integrating learning-based methods with classical control for autonomous systems.
Research Focus
Key Achievements
Top Papers
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