Papers
2
Total Citations
7
H-Index
2
About
Ling Song’s research bridges the critical intersection of human-robot interaction and intelligent control systems, with a focus on how design and autonomy shape user satisfaction. In her most-cited work, “Relationship Between Individual Perceptual Feature Demand and Satisfaction in the Small Assistant Robot Modeling Design” (2020, 4 citations), Song explores how users’ perceptual preferences directly influence their satisfaction with small assistant robots—a study that informs more empathetic, user-centered robot design. Her earlier foundational work, “Navigation Control of an Autonomous Robot Based on Chaos Immune Optimization Algorithm” (2012, 3 citations), demonstrates her technical depth in developing bio-inspired algorithms for robust, adaptive robot navigation. Together, these contributions highlight Song’s dual commitment to both the human experience and the algorithmic backbone of robotics. Her research offers practical insights for designers and engineers seeking to create robots that are not only functionally reliable but also emotionally resonant. By integrating perceptual modeling with advanced optimization techniques, Ling Song is helping to shape a future where robots are more intuitive, responsive, and satisfying to interact with.
Research Focus
Key Achievements
Top Papers
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- 2