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About

Yudai Okada is a robotics researcher whose work centers on bridging the gap between computer-aided manufacturing (CAM) and industrial robotic machining. His primary research areas include post-processor development, robot language generation, and data interpolation for automated manufacturing systems. Okada’s major contribution lies in creating a seamless, teaching-free interface for industrial robots—specifically the FANUC R2000iC—by developing post-processors that convert standard cutter location source (CLS) data directly into robotic programs. His most cited work, "Development of post-processor approach for an industrial robot FANUC R2000iC" (6 citations), established a foundational method for automating robot path generation without conventional manual teaching. In subsequent papers, he advanced this framework by introducing circular arc and spline curve interpolation techniques to smooth CLS data, enhancing machining precision for foamed polystyrene materials. Notably, his 2017 paper on spline interpolation for converting stereolithography data into CLS data further streamlined the CAM-to-robot pipeline, eliminating the need for specialized robot language expertise. Though his citation counts are modest, Okada’s focused contributions to robotic CAM systems offer practical, accessible solutions for small-scale and educational machining applications, demonstrating a clear impact on simplifying industrial robot programming.