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About

Zhilin Fan is a researcher specializing in multi-robot systems, reinforcement learning, and autonomous navigation. Their major contributions lie in developing advanced control methods for multi-robot coordination in complex, obstacle-filled environments. Fan’s most cited work, “Reinforcement learning method for target hunting control of multi‐robot systems with obstacles” (2022, 12 citations), introduces a novel approach to the target encirclement problem by modeling multi-robot interactions as a Markov game and designing potential energy models for efficient hunting. This work is complemented by two influential papers on formation and path planning: “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). These studies leverage a leader-following framework and enhanced Q-learning algorithms to enable robots to navigate unknown environments and maintain formation while avoiding concave obstacles. Fan’s research has significant implications for swarm robotics, search-and-rescue operations, and industrial automation, demonstrating how reinforcement learning can solve real-world coordination challenges. With a growing citation record, Fan is establishing themselves as a key contributor to the field of intelligent multi-robot systems.