Industrial Robot Force Control Evaluation for Parameter Tuning
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Solution Overview
Problem
Inexperienced operators face challenges in accurately adjusting parameters for force control in industrial robots, leading to inefficient operations and potential errors in subsequent processes, which can hinder machine learning and result in inaccurate learning models.
Innovation Solution
A determination apparatus that uses an evaluation function to assess the quality of operations based on data acquired during force control, enabling stable and standardized parameter adjustments without relying on operator experience, comprising a data acquisition unit, evaluation function creation unit, determination data creation unit, preprocessing unit, and learning unit to generate a learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter adjustment is performed manually by an inexperienced operator, then the operation can be executed, but the determination accuracy of parameter suitability deteriorates leading to errors in subsequent operations
Solution Approach 1:
An evaluation function acts as an intermediary between the operator and the force control parameters. This function automatically evaluates parameter suitability based on operation data, eliminating the need for operator judgment while maintaining ease of operation. The evaluation function processes operation data and provides objective feedback on parameter quality without requiring expert knowledge.
Solution Approach 2:
The system implements feedback by evaluating operation data through the evaluation function and using the results to guide parameter adjustment. The feedback mechanism provides continuous information about parameter suitability, allowing operators to make informed adjustments without needing extensive experience. The feedback loop ensures that parameter decisions are based on objective evaluation rather than subjective judgment.
2Productivity
If parameter adjustment is performed manually by trial and error, then the operation can be executed, but the time required for determination increases
Solution Approach 1:
The evaluation function performs preliminary evaluation of parameter suitability before actual force control execution. By assessing parameter quality in advance using operation data, the system avoids time-consuming trial and error during actual operations. The preliminary evaluation guides parameter selection, ensuring that suitable parameters are chosen before execution begins.
Solution Approach 2:
The system replaces manual trial-and-error adjustment with an automated evaluation mechanism. Instead of relying on physical trial and error processes that consume time, the evaluation function automatically assesses parameter suitability based on operation data, significantly reducing the time required for parameter determination while maintaining operation execution speed.
3Manufacturing precision
If force control is implemented with manual parameter setting, then the control function is achieved, but the manufacturing precision of the operation deteriorates due to incorrect parameter determination
Solution Approach 1:
The evaluation function performs self-service by automatically evaluating parameter suitability without requiring external expert judgment. The function uses operation data to assess parameters and provides objective feedback on quality, enabling the system to self-correct and maintain high manufacturing precision. This self-evaluation mechanism eliminates the need for complex manual adjustment procedures.
Solution Approach 2:
The evaluation function serves as an intermediary between the force control system and parameter adjustment processes. It objectively evaluates parameter quality based on operation data, providing guidance that ensures high manufacturing precision without requiring complex manual intervention. The intermediary function bridges the gap between automated control and quality assurance.
Data Source
AI summary
A determination apparatus acquires, as acquired data, the state of force and moment applied to a manipulator and the state of the position and the posture in an operation when a force control of an industrial robot is carried out, and creates an evaluation function that evaluates the quality of the operation of the industrial robot based on the acquired data. Then, it creates determination data for the acquired data using the evaluation function, and creates state data used for machine learning based on the acquired data. Then, it generates a learning model for determining a quality of an operation of the industrial robot using the state data and the determination data.