Robot Calibration Quality Assessment via Friction and CoM Estimation
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Solution Overview
Problem
Robots lack the sophistication to accurately and precisely execute complex tasks due to inadequate calibration, which affects their ability to mimic human-like interactions and movements in environments like warehouses and retail settings.
Innovation Solution
A computing system that divides sensor data into training and test data to estimate parameters such as friction and center of mass, predicts actuation data, and determines an error parameter to assess the accuracy of robot calibration, ensuring the quality and reliability of sensor data used for calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robot calibration is performed using traditional methods, then the calibration process can be completed, but the accuracy and precision of robot movement control remains insufficient
Solution Approach 1:
The patent implements feedback by using sensors to continuously monitor robot arm movement and joint actuation, comparing actual performance against calibration data, and using error parameter values to assess and improve calibration quality. This closed-loop feedback mechanism enables continuous refinement of calibration accuracy and movement control reliability.
Solution Approach 2:
The patent replaces traditional mechanical calibration methods with a computational approach using machine learning models. The system uses sensor data, movement data, and actuation data to train models that predict friction parameters and center of mass, substituting physical trial-and-error calibration with algorithmic computation to achieve higher precision.
2Manufacturing precision
If sophisticated robot calibration is implemented to improve movement accuracy, then the robot can better execute complex tasks, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the calibration system into distinct functional modules: sensor data acquisition, movement data collection, actuation data recording, machine learning model training, and error parameter calculation. Each module handles a specific aspect of the calibration process, making the overall complex system manageable and maintainable while achieving high movement precision.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw sensor data and calibration parameters. These models process and interpret complex sensor inputs, extracting meaningful information about friction and center of mass without requiring direct complex mechanical measurements, thereby simplifying the calibration system architecture.
3Adaptability or versatility
If traditional calibration methods are used, then the calibration process is simpler, but the robot lacks the sophistication to duplicate human interactions
Solution Approach 1:
The patent changes the calibration parameters from simple geometric measurements to complex physical properties including friction parameters and center of mass. By incorporating these additional parameters into the calibration process, the robot gains the sophistication needed to duplicate human interactions while maintaining measurement precision through advanced sensing and computation.
Data Source
AI summary
A computing system and method are presented. The computing system may store sensor data which includes: (i) a set of movement data, and (ii) a set of actuation data. The computing system may divide the sensor data into training data and test data by: (i) selecting, as the training data, movement training data and corresponding actuation training data, and (ii) selecting, as the test data, movement test data and corresponding actuation test data. The computing system may determine, based on the movement training data and the actuation training data, at least one of: (i) a friction parameter estimate or (ii) a center of mass (CoM) estimate, and may determine actuation prediction data based on the movement test data and based on the at least one of the friction parameter estimate or the CoM estimate. The computing system may further determine residual data, and determine a value for an error parameter.