Papers
4
Total Citations
17
H-Index
3
About
Yikun Zhang is a robotics researcher whose work focuses on advancing robotic manipulation and human-robot interaction for complex real-world applications. His key research areas include robotic trajectory planning, human-following control, and force-sensitive manipulation, with a particular emphasis on optimizing performance under practical constraints. Zhang's major contributions span multiple domains: he developed a multi-objective redundancy optimization method for continuous-point robot milling paths in shipbuilding, addressing the unique challenges of using 6-DOF manipulators for five-axis machining in large-scale manufacturing. He also proposed a time-jerk optimal trajectory planning approach using convex optimization, which simultaneously minimizes execution time and jerk while respecting kinematic and dynamic constraints. In human-robot interaction, Zhang introduced a relative-posture-fixed model predictive control method for human-following robots that maintains visibility in obstacle environments, and developed a learning-based variable admittance control combined with nonlinear model predictive control for contact force tracking in unknown environments. His work on shipbuilding robotics (7 citations) and trajectory optimization (5 citations) has already garnered attention, demonstrating the practical impact of his research on manufacturing and service robotics.
Research Focus
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