Robot Motion Control with Section-Based Vibration Learning

The patent segments the operation program into multiple sections with different priority levels. High-priority sections (where vibration suppression is critical) receive focused machine learning attention, while low-priority sections use standard control. This segmentation allows the system to achieve adequate vibration suppression in critical areas without requiring extensive machine learning repetitions across the entire operation program, thus maintaining productivity while reducing harmful vibrations where they matter most.

The patent applies local quality by differentiating the level of machine learning application across different sections of the operation program. Instead of uniformly applying extensive machine learning to all sections, the system identifies specific high-priority sections where vibration suppression is critical and applies focused machine learning only to those sections. This approach achieves adequate overall vibration suppression while significantly reducing the total time required for machine learning repetitions.

The patent replaces mechanical solutions (increasing robot rigidity) with a control-based solution (section-prioritized machine learning). Instead of modifying the robot's mechanical structure to reduce vibration, the system uses intelligent control that identifies and suppresses vibrations in high-priority sections through targeted machine learning. This substitution avoids increasing mechanical complexity and cost while achieving effective vibration reduction where it is most needed.