Yiran Cheng

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About

Yiran Cheng is a researcher whose work sits at the intersection of intelligent robotics, optimization algorithms, and assistive technologies for aging populations. Their key research areas include swarm intelligence, path planning, and mobile robot navigation in complex environments. Cheng’s most notable contribution is the development of a quantum particle swarm optimization (QPSO) algorithm tailored for mobile robot path planning in elderly care settings. This work addresses the computationally challenging NP-hard problem of navigating community environments for pension robots, offering a more robust solution to local optimization than traditional swarm intelligence methods. While their highly cited paper, "Research on quantum particle swarm optimization in mobile robot path planning for aged service" (2018), has accumulated 5 citations, it represents a meaningful step toward integrating advanced metaheuristic algorithms with socially impactful applications. Cheng’s research bridges theoretical optimization and practical robotics, aiming to enhance the autonomy and safety of service robots for the elderly. Their work is particularly relevant for students and researchers interested in the convergence of artificial intelligence, healthcare robotics, and real-world optimization challenges.