Yingxu Dai

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About

Yingxu Dai is a researcher specializing in robotics and intelligent optimization algorithms, with a primary focus on advancing robot path planning through computational intelligence. Their most notable contribution is the development of an improved genetic algorithm that addresses critical limitations in traditional approaches, including the blindness and randomness of initial population generation, excessive path turning points, and susceptibility to local optimization traps. By incorporating prior knowledge into the initialization process, Dai’s work enhances the efficiency and smoothness of autonomous navigation, offering practical solutions for real-world robotic systems. Although their key paper, "Research on Robot Path Planning Based on Improved Genetic Algorithm" (2022), has garnered 2 citations, it represents a foundational step in refining evolutionary methods for mobile robotics. Dai’s research bridges the gap between theoretical algorithm design and applied robotics, demonstrating a commitment to solving tangible engineering challenges. Their work is particularly relevant for students and researchers exploring metaheuristic optimization, autonomous systems, and the intersection of artificial intelligence with mechanical control.