Cable State Evaluation Using Machine Learning in Robots
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Solution Overview
Problem
There is a need for a technique to accurately and quantitatively evaluate the state of cables in robots, as existing methods lack precision and reliability in monitoring cable conditions during operations.
Innovation Solution
A machine learning apparatus that acquires image data of cables while a robot performs predetermined operations and generates a learning model representing the correlation between image data and cable states, using a camera and processor to classify normal or abnormal cable conditions through supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cable monitoring methods are used, then the system is simple to implement, but the measurement precision and reliability of cable state evaluation are insufficient
Solution Approach 1:
The patent replaces conventional mechanical or electrical cable monitoring methods with an optical-based machine learning system. A camera captures images of the cable, and a machine learning model processes these images to evaluate cable state, substituting physical sensing mechanisms with optical imaging and computational analysis to achieve higher measurement precision
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw cable images and cable state evaluation. The model learns the mapping relationship from image data to cable conditions through training, serving as an intelligent intermediary that translates visual information into diagnostic insights without direct physical contact with the cable
2Measurement precision
If machine learning model is trained with comprehensive learning data, then the measurement precision improves, but the loss of time for data collection and model training increases
Solution Approach 1:
The patent performs preliminary action by collecting and organizing learning data in advance, including normal and abnormal cable images with corresponding state information. The machine learning model is trained offline before actual cable monitoring begins, so that during operation, only inference is needed rather than full retraining, significantly reducing real-time processing time while maintaining high accuracy
Solution Approach 2:
The patent changes parameters by using image data as input features for the machine learning model, transforming the cable monitoring problem into an image recognition task. This parameter transformation allows the system to leverage computer vision techniques and process cable states through learned visual patterns rather than traditional sensing parameters
Data Source
AI summary
A machine learning apparatus that can quantitatively and accurately evaluate the state of a cable in a robot. A machine learning apparatus for learning a state of a cable mounted in a robot includes a learning data acquisition section that acquires, as a learning data set, image data of the cable captured by a camera while the robot performs a predetermined operation, and data representing a state of the cable while the robot performs the predetermined operation, and a learning section that generates a learning model representing a correlation between the image data and the state of the cable, using the learning data set.