Shuaibing Chang
Papers
2
Total Citations
5
H-Index
2
About
Shuaibing Chang is a robotics researcher whose work centers on intelligent perception, manipulation, and reconfigurable mobility for service robots. Chang’s primary contributions address two critical challenges in domestic robotics: precise object recognition and adaptive locomotion. In their highly cited 2024 paper on target recognition and grasping, Chang proposed a deep learning-based method using the YOLOv8 algorithm to identify and grasp four types of household objects, significantly improving accuracy and stability for dual-arm cooperative mobile robots. This work, already garnering 3 citations, directly tackles the real-world problems of low grasping precision and insufficient stability in home environments. Complementing this, Chang’s research on omni-directional mobile reconfigurable robots (OMRR) introduced a novel design featuring a symmetrical McNamum wheel chassis and Arduino-based control, enabling stable, balanced traction and reconfigurable mobility. With 2 citations, this work advances the mechanical design and control of versatile robotic platforms. Together, Chang’s contributions demonstrate a clear focus on integrating vision-based perception with adaptive hardware, laying essential groundwork for more capable and reliable service robots in everyday settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2