Robot Tool State Detection Using Force-Based Machine Learning
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Solution Overview
Problem
Current techniques fail to accurately determine the state of a tool used by a robot based on the force applied during operations, lacking a reliable method to quantify the correlation between force and tool state.
Innovation Solution
A machine learning apparatus that acquires data of the force applied from the tool to the robot during predetermined operations and generates a learning model representing the correlation between the force and the tool's state, using this data to determine whether the tool is in a normal or abnormal condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a technique is used to learn characteristics of sound or vibration to determine tool deterioration, then tool state can be assessed, but the method cannot accurately determine tool state based on force applied during robot operations
Solution Approach 1:
The patent replaces acoustic and vibration-based detection methods with a force-based detection system. By using force sensors to measure the force applied from the tool to the robot during operations, and applying machine learning to this force data, the system achieves more accurate and reliable tool state determination compared to traditional sound/vibration analysis.
Solution Approach 2:
The system enables the robot to automatically determine tool state by itself through machine learning. The robot collects force data during its own operations, processes this data through a learning model, and autonomously determines tool state without requiring external inspection equipment or manual assessment.
2Measurement precision
If machine learning is applied to force data to determine tool state, then automatic and accurate quantification is achieved, but the complexity of the system increases
Solution Approach 1:
The patent introduces a force sensor as an intermediary device between the tool and robot. This sensor serves as a mediator that converts complex tool state information into measurable force data, which can then be processed by machine learning algorithms. This intermediary approach simplifies the overall system architecture while maintaining high measurement precision.
Solution Approach 2:
The system transforms the assessment of tool state from qualitative acoustic/vibration characteristics to quantitative force parameters. By measuring force magnitude and direction during robot operations, the system converts complex tool condition assessment into straightforward parameter measurement and analysis.
Data Source
AI summary
A machine learning apparatus that can determine the state of a tool from a force applied from the tool to a robot while the robot performs a work using the tool. A machine learning apparatus for learning a state of a tool used for a work by a robot includes a learning data acquisition section that acquires, as a learning data set, data of a force applied from the tool to the robot while the robot causes the tool to perform a predetermined operation, and data indicating the state of the tool during the predetermined operation, and a learning section that generates a learning model representing a correlation between the force and the state of the tool, using the learning data set.