Papers
3
Total Citations
32
H-Index
2
About
Ling Tang is a robotics researcher whose work centers on robotic arm vision, motion planning, and dynamic grasping—critical areas for advancing industrial automation and human-robot collaboration. Tang’s most influential contribution is the development of an improved SURF (Speeded-Up Robust Features) algorithm for binocular visual systems in robotic arms, published in 2019 and garnering 26 citations. This work addresses the fundamental challenge of enabling robots to perceive and interact with their environment more accurately, a cornerstone of modern high-tech development. Building on this foundation, Tang has pioneered real-time motion planning frameworks for dynamic environments. The 2023 paper on “Dynamic grasping of manipulator based on realtime smooth trajectory generation” introduces a novel sequential Sense-Plan-Act (SeqSPA) framework that generates smooth, safe grasping trajectories for moving targets, while the 2022 work proposes an efficient online method combining front-end pathfinding with back-end nonlinear optimization to pick up moving objects under temporal constraints. Though early in citation accumulation, these contributions demonstrate Tang’s commitment to solving the practical, real-world challenge of enabling robotic arms to operate reliably in unpredictable, dynamic settings—a key step toward more autonomous and responsive industrial systems.
Research Focus
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Top Papers
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