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About

Zifan Fang is a robotics researcher whose work centers on robot kinematics, perception, and autonomous manipulation. His most significant contribution lies in advancing inverse kinematic solutions for robotic arms, where he developed an improved particle swarm algorithm that overcomes the limitations of traditional analytical and geometric methods—a paper that has garnered 78 citations and is foundational for robot control and path planning. Fang has also pioneered the construction of digital twin models for robots and multi-object stacking environments, enabling more effective grasp planning in cluttered settings. More recently, his work on real-time visual SLAM using YOLO-Fastest addresses the critical challenge of dynamic environments in autonomous navigation, achieving 14 citations since 2024. His research directly impacts practical robotics applications, from industrial automation to autonomous systems. With a growing citation record and a focus on bridging simulation and real-world performance, Fang is establishing himself as a rising figure in intelligent robotics and perception systems.