Xiyong Tang
Papers
1
Total Citations
5
H-Index
1
About
Xiyong Tang is a researcher advancing the integration of machine vision and evolutionary algorithms to enhance industrial robotics. His work focuses on visual localization techniques that enable robots to accurately perceive and interact with their environment, a critical capability for automated assembly and manufacturing. Tang’s most-cited paper, “Visual Localization of Workpiece based on Evolutionary Algorithm and its Application in Industrial Robot” (2023), demonstrates how combining machine vision with evolutionary optimization improves a robot’s ability to determine workpiece position and orientation, boosting system intelligence and automation. This approach addresses key challenges in precision manufacturing, where reliable visual feedback is essential for tasks like pick-and-place and assembly. With 5 citations, his research contributes to the growing field of smart robotics, where adaptive algorithms replace traditional fixed-programming methods. Tang’s work has practical implications for industries seeking to increase production flexibility and reduce human error. By bridging computer vision and optimization theory, he helps pave the way for more autonomous, efficient industrial systems—a vital step toward the factories of the future.
Research Focus
Key Achievements
Top Papers
- 1