Can Fang

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About

Can Fang is a rising researcher in robotics and autonomous navigation, whose work focuses on advancing path-planning algorithms for mobile robots under real-world kinematic constraints. Fang’s major contributions lie in improving the efficiency and quality of Rapidly-exploring Random Tree (RRT)-based methods. In their 2024 paper, they introduced an enhanced RRT* algorithm that integrates clothoid curves to generate smoother, more feasible paths, addressing critical limitations in robot maneuverability. This work has already garnered 14 citations, reflecting its immediate relevance. Building on this, Fang’s 2025 study, “FHQ-RRT*,” tackles the persistent challenges of slow path acquisition and high path costs in traditional RRT* approaches, offering a faster route to high-quality trajectories. With 10 citations in its first year, this innovation underscores Fang’s ability to produce impactful, solution-oriented research. Their work is particularly notable for bridging theoretical algorithm design with practical robotic applications, making autonomous systems more reliable and efficient. For students and researchers exploring motion planning, Fang’s contributions offer a clear, rigorous path forward in optimizing robot navigation under constraints.