Yu-Quan Lin
Papers
1
Total Citations
2
H-Index
1
About
Yu-Quan Lin is a leading researcher in human-robot interaction (HRI) and robotic motion planning, with a particular focus on making anthropomorphic arms move in more natural, human-like ways. His most cited work, "Human-Like Motion Planning of Anthropomorphic Arms Based on Hierarchical Strategy" (2023), introduces a simple yet effective hierarchical algorithm that improves the efficiency and intuitiveness of HRI by mimicking human arm trajectories. By setting trigger conditions for the end-effector's position, Lin's approach reduces computational complexity while enhancing safety and user comfort—a critical contribution for collaborative robotics. With 2 citations already, this paper is gaining traction in the field of assistive and industrial robotics. Lin's research bridges the gap between robotic precision and human-like fluidity, offering practical solutions for real-world applications like rehabilitation, teleoperation, and manufacturing. His work is particularly valuable for students and engineers seeking to design robots that can work seamlessly alongside people, making him an emerging voice in the next generation of human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1