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About

Mao Yang is a robotics and autonomous systems researcher whose work centers on multi-robot coordination, task allocation, and intelligent control. His most recognized contribution, "Multi-robot task allocation for fire-disaster response based on reinforcement learning" (2009, 37 citations), demonstrated how distributed reinforcement learning could enable dynamic, efficient task distribution across robot teams in emergency scenarios — a practically significant advance for disaster robotics. Building on this foundation, Yang has explored diverse coordination paradigms, including particle swarm optimization for multi-robot path planning (18 citations), game-theoretic task allocation using Nash equilibrium, and cooperative Q-learning strategies designed to accelerate convergence in multi-agent systems. His interest in collective behavior is reflected in several studies examining swarm robotics through the Vicsek model and fuzzy logic frameworks, addressing synchronization and flocking dynamics with mathematical rigor. Yang has also ventured into biped robot control, proposing an adaptive excitation method inspired by biological locomotion principles to stabilize chaotic gaits. Across these contributions, his research consistently bridges theoretical modeling and practical robotics applications, making him a notable voice in multi-robot systems research, particularly at the intersection of machine learning, swarm intelligence, and autonomous coordination.