Robot Controller Learning Speed-Up Ratios for Vibration Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot controllers require additional sensors to manage vibration and are inefficient in speeding up robot motion when the position and orientation of the robot change, relying heavily on operator skill and experience.
Innovation Solution
A robot controller that automatically generates motion patterns and learns a speed-up ratio for each pattern by adjusting velocity and acceleration, allowing the robot to operate efficiently across a predetermined working region without the need for special vibration-restricting sensors, by dividing the working region into smaller areas and using feedback from motor commands and actual values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If additional sensors are added to restrain robot vibration with high accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The robot controller performs self-learning by automatically generating motion patterns and calculating speed-up ratios based on motor load and vibration characteristics during normal operation. The system uses its own operational data to improve positioning accuracy without requiring external sensors or manual intervention, thereby resolving the contradiction between precision and complexity
Solution Approach 2:
The learning control part continuously monitors motor load and vibration characteristics during robot operation, compares actual motion with target motion, and automatically adjusts speed-up ratios for different motion patterns. This closed-loop feedback mechanism enables high-precision positioning without additional sensors by utilizing existing system responses
2Speed
If operator skill and experience are relied upon to modify teaching programs, then robot motion velocity can be improved, but ease of operation deteriorates
Solution Approach 1:
The robot controller automatically learns and determines optimal speed-up ratios for various motion patterns by analyzing motor load and vibration characteristics during operation. This self-service capability eliminates the need for operators to manually adjust teaching programs based on experience, thereby improving both ease of operation and motion velocity through automated optimization
Solution Approach 2:
The system replaces manual operator judgment and experience with automated computational algorithms that analyze motor current, vibration characteristics, and motion patterns to determine optimal speed-up ratios. This substitution of mechanical/electrical measurement and calculation eliminates subjective operator dependency while improving teaching efficiency
3Speed
If the same motion is repeatedly learned to increase robot velocity, then speed is improved, but adaptability deteriorates
Solution Approach 1:
The working region is divided into multiple divided regions, and motion patterns are segmented and learned independently for each region. The system generates motion patterns for arbitrary positions and orientations within each divided region, enabling the robot to adapt to different positions and orientations while maintaining high speed through region-specific speed-up ratios
Solution Approach 2:
The system dynamically generates motion patterns based on the robot's current position and orientation within the working region. Rather than relying on fixed repeated motions, the controller adapts motion patterns in real-time by selecting appropriate divided regions and applying learned speed-up ratios, thereby maintaining both speed and adaptability
Data Source
AI summary
A robot controller capable of easily speeding up the motion of a robot by learning, without using a special device and teaching know-how. The robot controller has a storing part which stores a reference motion pattern of the robot; an inputting part which designates at least one of a working start region where a motion of the robot based on the reference motion pattern is initiated and a working end region where the motion of the robot is terminated; an automatic generating part which automatically generates a plurality of motion patterns of the robot based on the reference motion pattern and divided regions formed by dividing a working region at a predetermined resolution; and a learning control part which learns a motion speed-up ratio for speeding up the motion by changing a velocity or acceleration in relation to each of the automatically generated motion patterns.