Kim Michael
Papers
1
Total Citations
3
H-Index
1
About
Kim Michael’s research lies at the intersection of robotics, computer vision, and automated manufacturing, with a particular focus on sensor-guided manipulation. His most cited work, “The use of visual feedback for the acquisition of pseudorandomly oriented parts” (1984, 3 citations), addresses the foundational challenge of enabling robots to pick up cylindrical objects from unstructured environments. In this study, Michael details how a vision system can define object positions within a workcell, integrating image analysis, camera calibration, and algorithmic control to guide a robot arm in real time. Though modest in citation count, this paper is a notable early contribution to vision-based robotic grasping—a field that would later explode in importance with the rise of industrial automation and intelligent manufacturing. Michael’s work demonstrates a pragmatic, systems-level approach: rather than focusing solely on theoretical vision algorithms, he tackles the full pipeline from sensor input to physical action. For students and researchers exploring the history of robotic perception, Michael’s research offers a clear window into the practical challenges of marrying sight with motion, laying groundwork for today’s more sophisticated pick-and-place systems.
Research Focus
Key Achievements
Top Papers
- 1