Qiang Bai
Papers
9
Total Citations
355
H-Index
6
About
Qiang Bai is a leading researcher at the intersection of machine vision, deep learning, and intelligent robotics, with a primary focus on enabling robots to perceive, plan, and grasp with human-like precision. His most influential work, a 2020 survey on machine learning for object detection and robot grasping, has garnered 145 citations and serves as a foundational reference in the field. Bai’s major contributions include developing an improved YOLOv5-based detection method for grasping robots (97 citations), which directly addresses the industrial challenges of inaccurate positioning and low recognition efficiency. He has also advanced mobile robot navigation through intelligent optimization algorithms (51 citations) and enhanced manipulator trajectory planning using RBF neural networks (34 citations). More recently, Bai has explored deep learning for real-life grasping scenarios, real-time motion tracking with the Baxter robot, and point cloud grasping within the Internet of Things. His work on three-finger gripper strategies using DeepLabV3+ semantic segmentation further demonstrates his commitment to achieving stable, human-like grasp capabilities. Through these innovations, Bai is systematically pushing the boundaries of autonomous robotic manipulation in both industrial and everyday environments.
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
- 4Trajectory Planning of Robot Manipulator Based on RBF Neural Network34 citations · 2021
- 5Research on Robot Grasping Based on Deep Learning for Real-Life Scenarios11 citations · 2023
- 6
- 7Research on Intelligent Robot Point Cloud Grasping in Internet of Things5 citations · 2022
- 8
- 9Robot Three-Finger Grasping Strategy Based on DeeplabV3+2 citations · 2021