Lingjun Shao

Huazhong University of Science and Technology

Papers

Total Citations

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About

Lingjun Shao is a robotics researcher whose work centers on intelligent robotic assembly, particularly in challenging industrial environments. Their key research areas include force-guided manipulation, geometric reasoning, and sensor-based control for precision tasks. Shao's major contribution lies in developing methods that enable robots to perform complex assembly operations—such as mating electrical connectors—even when faced with large initial deviations, confined spaces, and poor visual conditions. Their most-cited paper, "Geometry and Force Guided Robotic Assembly With Large Initial Deviations for Electrical Connectors" (2024), demonstrates a novel approach that combines geometric models with real-time force feedback to overcome visual occlusions and lighting challenges common in real-world manufacturing. This work has already garnered 4 citations, signaling its relevance to both academia and industry. By addressing the critical gap between theoretical robotics and practical assembly constraints, Shao is helping to make flexible automation viable for tasks previously requiring human dexterity. Their research holds promise for advancing autonomous manufacturing, particularly in sectors like automotive and electronics where reliable connector assembly is essential.