Hejing Ling
Papers
1
Total Citations
2
H-Index
1
About
Hejing Ling’s research advances the frontier of human-robot cooperation (HRC), with a central focus on ensuring human safety in shared environments. Her most-cited work, “Motion Planning Combines Human Motion Prediction for Human-Robot Cooperation” (2022), tackles a critical challenge: enabling robots to anticipate and adapt to human movements in real time. Unlike conventional motion planning algorithms that treat dynamic obstacles as passive, Ling’s approach integrates human motion prediction directly into the planning loop, allowing robots to proactively avoid collisions while maintaining task efficiency. This contribution addresses an ongoing safety problem in human-robot coexisting scenarios, where even minor missteps can lead to injury. With 2 citations to date, her paper has already sparked interest among researchers working on safe, interactive robotics. Ling’s work is particularly notable for bridging the gap between predictive modeling and practical motion control, offering a framework that could be extended to manufacturing, healthcare, and service robotics. Her research underscores a commitment to designing robots that are not only capable but also trustworthy partners, making her a rising voice in the field of human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1