Xiaomeng Zhang
Papers
1
Total Citations
9
H-Index
1
About
Xiaomeng Zhang is a researcher whose work sits at the intersection of robotics, artificial intelligence, and swarm systems. Their most notable contribution focuses on enhancing reinforcement learning frameworks for multi-robot coordination, specifically through the development of an improved Q-Learning algorithm that incorporates a pheromone mechanism inspired by biological swarm behavior. This innovative approach addresses fundamental challenges in autonomous robot navigation, including path planning and motion control, by combining the convergence properties of reinforcement learning with biologically inspired communication strategies drawn from ant colony optimization principles. The work demonstrates how nature-inspired algorithms can meaningfully improve the adaptability and efficiency of swarm robot systems operating in complex, dynamic environments. With 9 citations, this research has contributed to the growing body of literature exploring hybrid AI methodologies for autonomous systems. Zhang's work is particularly relevant for researchers and students exploring how classical reinforcement learning techniques can be augmented with stigmergic communication models to produce more robust and scalable multi-agent robotic systems — a challenge that remains highly pertinent as swarm robotics continues to expand into real-world applications.
Research Focus
Key Achievements
Top Papers
- 1