Robot Teaching Data Thinning for Precise Motion Playback
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Solution Overview
Problem
Existing robot systems face inefficiencies in motion teaching due to the need to process and store large amounts of data points, which can lead to extraneous movements and reduced operability, especially when teaching complex operations like polishing.
Innovation Solution
The system acquires teaching position and force data, generates thinned data by removing non-influential points, and uses this data to create position and force commands for the robot, allowing for more efficient operation by reducing the number of data points and focusing on key movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If teaching position data with many data points is used, then the accuracy of demonstrating complex operations is improved, but the operability and efficiency of the teaching process deteriorates due to extraneous movements and large data processing requirements
Solution Approach 1:
The patent extracts and removes extraneous teaching position data points that do not contribute to essential robot movements. By identifying and eliminating redundant data points while preserving key positional information, the system maintains demonstration accuracy while reducing data volume and improving teaching operability
Solution Approach 2:
The patent segments the teaching position data into essential and non-essential components. By dividing the data set and selectively processing only the critical segments that define meaningful robot movements, the system achieves efficient data handling without sacrificing operational accuracy
2Measurement precision
If teaching position data with many data points is used, then the accuracy of demonstrating complex operations is improved, but the teaching process efficiency deteriorates due to large data processing requirements
Solution Approach 1:
The patent extracts only the essential teaching position data points needed for accurate robot operation. By removing unnecessary data points through automated analysis of movement significance, the system reduces processing workload and accelerates the teaching process while preserving operational accuracy
Solution Approach 2:
The patent changes the parameter of data point density by selectively reducing the number of teaching positions. This parameter transformation maintains the essential information content while optimizing the data volume for efficient processing and faster teaching completion
3Reliability
If all teaching positions are processed, then complete coverage of motion paths is achieved, but the system complexity and data processing burden increase
Solution Approach 1:
The patent extracts and eliminates redundant teaching positions that do not contribute to complete motion coverage. By identifying and removing duplicate or unnecessary data points while preserving essential positional information, the system maintains reliable motion coverage with reduced data processing complexity
Data Source
AI summary
A robot system includes circuitry. The circuitry may be configured to acquire teaching position data including a plurality of teaching positions arranged in time series based on the demonstration data of the operator. The circuitry may be further configured to generate thinned position data obtained by removing at least one of the teaching positions from the teaching position data. The circuitry may be further configured to generate a position command based on the thinned position data. The circuitry may be further configured to operate the work robot based on the position command.