Papers
5
Total Citations
36
H-Index
3
About
Xiaolin Ren is a roboticist whose research focuses on the critical intersection of visual servoing, decentralized control, and human-robot interaction for modular manipulators. Ren’s most impactful work, “Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints” (22 citations), tackles the fundamental challenge of maintaining image features within the camera’s field of view under non-Gaussian noise, proposing a hybrid algorithm to compute the interaction matrix without requiring precise calibration. This contribution is essential for deploying robots in unstructured environments. Ren further advances the field with an observer-critic-based event-triggered decentralized optimal control strategy for modular robot manipulators, integrating joint torque feedback to achieve efficient, scalable tracking control. In the domain of physical human-robot interaction, Ren developed a decentralized robust control method that estimates human motion intention, enabling safer and more intuitive collaboration without relying on a precise centralized dynamic model. Complementing these theoretical contributions, Ren conducted foundational simulation research on the PUMA 560 manipulator’s kinematics and trajectory planning, and explored Kalman and smooth variable structure filters for robust real-time pose estimation in visual servoing. Through this body of work, Ren is shaping the future of adaptive, safe, and vision-guided robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5