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About

Yang-Yang Chen is a researcher specializing in multi-robot coordination, control theory, and deep reinforcement learning, with a particular focus on industrial applications such as welding and formation control. Their major contributions include developing adaptive fault-tolerant formation tracking control for networked mobile robots under input delays, which has garnered 11 citations, and pioneering the use of multi-agent deep deterministic policy gradient (MADDPG) and QMIX algorithms for coordinated welding tasks, achieving 10 and 4 citations respectively. Chen has also advanced hierarchical consensus methods for constrained second-order multi-agent systems, enabling formation control of multiple mobile robots, and has explored energy-efficient ship welding through reinforcement learning. Their work on cooperative task assignment and path planning, using A*-market-based and genetic algorithms, further demonstrates their versatility. With a total of over 40 citations across their top papers, Chen’s research bridges theoretical control systems and practical robotics, offering innovative solutions for real-world multi-robot challenges. Their notable achievements include addressing continuous state-action spaces and local observation constraints, making their work highly relevant for students and researchers in robotics and automation.