Yucheng Ling

Papers

Total Citations

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About

Yucheng Ling is a robotics researcher specializing in human-robot interaction, autonomous underwater systems, and real-time perception for manipulation tasks. Their work centers on developing intelligent control architectures that enable robots to collaborate with humans and operate effectively in dynamic, unstructured environments. Ling’s most cited paper, “A Haptic Shared Control Architecture for Tracking of a Moving Object” (2022, 13 citations), introduces a novel framework that fuses human input with robotic autonomy using haptic feedback and Kalman filter-based uncertainty estimation, while also incorporating obstacle avoidance via artificial potential fields. This work has laid important groundwork for safer, more intuitive human-robot collaboration. In their more recent contribution, “YOLO-Based 3D Perception for UVMS Grasping” (2024, 6 citations), Ling advances underwater robotics by developing YOLOv5s-CS, an enhanced object detection and 3D localization algorithm tailored for Underwater Vehicle-Manipulator Systems. This innovation directly addresses the challenge of precise grasping in low-visibility, high-pressure aquatic environments. With a growing citation impact and a clear focus on bridging perception and control, Ling’s research is shaping the future of shared autonomy and autonomous underwater intervention.