Silvia Lins
Papers
5
Total Citations
42
H-Index
4
About
Silvia Lins is a researcher advancing the frontier of safe human-robot collaboration (HRC) in industrial environments. Her work focuses on integrating deep learning and machine learning techniques to create intelligent systems that can detect and prevent collisions between humans and robots. Lins’s most influential paper, “Modeling and assessing an intelligent system for safety in human-robot collaboration using deep and machine learning techniques” (15 citations), establishes a comprehensive framework for real-time hazard detection. She further refines this approach in “A New Mechanism for Collision Detection in Human–Robot Collaboration using Deep Learning Techniques” (13 citations), introducing novel algorithmic architectures. Her research demonstrates that deep learning models can effectively assess collision risks in dynamic factory settings, as shown in her 2020 study (6 citations). Beyond safety, Lins has contributed to practical robotics applications, including gripper design for autonomous radio base station maintenance and the development of the RBOT system. Her work bridges the gap between theoretical AI safety models and tangible industrial automation solutions, making her a key voice in the push toward safer, more intelligent collaborative workspaces.
Research Focus
Key Achievements
Top Papers
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- 4Gripper Design for Radio Base Station Autonomous Maintenance System5 citations · 2021
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