Yifan Su

Shenyang Ligong University, Shenyang Institute of Automation, Xiamen University of Technology

Papers

Total Citations

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About

Yifan Su is a robotics and autonomous systems researcher whose work centers on mobile robot navigation, path planning, and motion control in complex environments. Su's most significant contributions lie in advancing artificial potential field (APF) methods and search-based algorithms to address longstanding limitations in robot autonomy. In a highly cited 2020 study (27 citations), Su proposed a reconstructed potential field function model that effectively resolves critical flaws in traditional APF approaches, including gravity imbalance, local minima, and oscillation — challenges that had long hindered real-world deployment. Complementing this, a companion 2020 paper (19 citations) introduced an improved A* algorithm capable of generating smoother, more efficient paths, particularly in challenging U-shaped terrain configurations where conventional methods falter. More recently, Su's 2025 work integrates demonstration learning with dynamic obstacle avoidance, addressing the sophisticated challenge of enabling robots to track demonstrated trajectories while responding intelligently to moving hazards in real time. Collectively, Su's research has meaningfully advanced the reliability and adaptability of autonomous mobile robots, making meaningful contributions to a field with broad applications in industrial automation, service robotics, and intelligent transportation.