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About

Yilin Han is a researcher specializing in multi-robot systems, reinforcement learning, and autonomous navigation. Their major contributions lie in developing intelligent control methods for multi-robot formation, path planning, and target hunting in complex environments. Han’s most-cited work, "Reinforcement learning method for target hunting control of multi‐robot systems with obstacles" (2022, 12 citations), introduces a Markov game-based approach that uses potential energy models to enable robots to cooperatively encircle targets while avoiding obstacles. This work addresses a critical challenge in swarm robotics. Additionally, Han has advanced formation control with "Improved Q‐Learning Method for Multirobot Formation and Path Planning with Concave Obstacles" (2021, 7 citations) and "A Path Planning Method for Multi-robot Formation Based on Improved Q-Learning" (2021, 6 citations), which enhance leader-follower strategies for navigating unknown environments. By integrating reinforcement learning with multi-agent coordination, Han’s research provides scalable, adaptive solutions for real-world applications like search-and-rescue and automated logistics. Their work is foundational for students and researchers exploring decentralized robotic systems and learning-based control.