Dan-Dan Wu
Papers
2
Total Citations
56
H-Index
2
About
Dan-Dan Wu is a researcher specializing in swarm intelligence and multi-robot systems, with a primary focus on path planning and task allocation. Her most significant contribution is an improved Particle Swarm Optimization (PSO) method for mobile robot path planning, published in 2020, which has garnered 53 citations. This work addresses critical limitations in traditional PSO algorithms—specifically, slow convergence and restricted applicability—by integrating refined initialization and update mechanisms to generate more efficient, collision-free paths. Wu further extends her expertise to multi-robot coordination, proposing a Global Optimal Evaluation of Revenue model for multi-object allocation. This framework tackles the challenge of achieving convergent, globally optimal assignments in complex, multi-objective environments, a problem that often stymies conventional swarm intelligence algorithms. Her research is notable for bridging theoretical optimization with practical robotic applications, offering scalable solutions for autonomous navigation and cooperative task execution. Wu’s work is highly relevant for students and researchers in robotics, artificial intelligence, and operations research, providing foundational methods for advancing autonomous systems in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
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