Shaobo Li
Papers
17
Total Citations
466
H-Index
8
About
Shaobo Li is a prominent robotics and machine learning researcher whose work sits at the intersection of computer vision, intelligent control, and autonomous robotic systems. His research has made significant contributions to object detection and robotic grasping, most notably through a widely cited 2020 survey on machine learning-based object detection and robot grasping (145 citations), which has become an essential reference for researchers entering the field. Building on this foundation, Li developed an improved YOLOv5-based detection method tailored for industrial grasping robots (97 citations), directly addressing real-world challenges of positioning accuracy and recognition efficiency. His broader robotics portfolio spans mobile robot path planning using intelligent optimization algorithms (51 citations), RBF neural network-based manipulator trajectory planning (34 citations), and privacy-conscious embarrassing situation detection for social robots in smart home environments (44 citations). More recently, Li has advanced sophisticated control theory, publishing adaptive fixed-time and fault-tolerant control frameworks for flexible-joint manipulators and aerial robotic systems. With over 440 cumulative citations across his major works, Li's research consistently bridges theoretical rigor with practical industrial application, making him a valuable voice in modern intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Object Detection Method for Grasping Robot Based on Improved YOLOv597 citations · 2021
- 3Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot51 citations · 2021
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- 5Trajectory Planning of Robot Manipulator Based on RBF Neural Network34 citations · 2021
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