Robot-to-Mobile Apparatus Coordinate Calibration Using Marker Poses
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Solution Overview
Problem
Current methods for calibrating robot and movable apparatus coordinate systems, such as those using augmented reality, require manual intervention and skilled users, leading to inaccuracies and usability issues.
Innovation Solution
A method that automatically determines the relation between a robot coordinate system and a movable apparatus coordinate system using a sensor and localization mechanism, positioning a marker in various poses to calculate transformations between different coordinate systems without human interaction, enabling accurate calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used to adjust coordinate systems visually, then the process can be performed with simple equipment, but the measurement precision and reliability are insufficient
Solution Approach 1:
The calibration system performs self-calibration by automatically capturing images of the marker from multiple poses, computing transformation matrices through coordinate system relations, and determining the robot-Movable Apparatus relationship without human intervention. The system serves itself by using its own sensor and processor to complete the calibration task that previously required skilled manual operation.
Solution Approach 2:
The patent replaces manual mechanical adjustment and visual estimation with an automated optical-mechanical system. Instead of manually moving components and visually aligning coordinate systems, the system uses a sensor to capture images, a processor to compute transformations, and mathematical relations between coordinate systems to achieve precise calibration automatically.
2Productivity
If automated calibration methods are implemented, then productivity and ease of operation improve, but device complexity increases
Solution Approach 1:
The Movable Apparatus serves multiple functions: it acts as both the platform carrying the sensor and the reference frame for calibration. The sensor both captures images of the marker and provides the coordinate system data needed for transformation calculations. This multi-functionality reduces the need for separate dedicated calibration equipment, managing complexity while enabling automation.
Solution Approach 2:
The marker serves as an intermediary object that bridges the robot coordinate system and the Movable Apparatus coordinate system. By attaching the marker to the robot and capturing its images from different poses using the Movable Apparatus sensor, the system creates a common reference that enables automatic computation of the transformation relationship without direct complex interaction between the two coordinate systems.
3Measurement precision
If multiple poses are captured for calibration, then measurement precision improves, but loss of time increases
Solution Approach 1:
The calibration process captures images of the marker in multiple different poses continuously, with the Movable Apparatus moving to various positions while the sensor continuously or sequentially captures the marker's position. This continuous capture of useful data from multiple poses enables precise transformation calculation without significant time loss, as the system efficiently collects all necessary calibration data in a streamlined sequence.
Data Source
AI summary
A method and a control arrangement for determining a relation R↔MA between a robot coordinate system of a robot and an MA coordinate system of a moveable apparatus, the movable apparatus including a sensor device and a localization mechanism configured to localize a sensor coordinate system of the sensor device in the MA coordinate system, wherein a marker is arranged in a fixed relation with a reference location on the robot. The method includes positioning the marker in a plurality of different poses in relation to the robot coordinate system. For each pose of the plurality of different poses, the method includes: determining, on the basis of sensor information, a relation C↔M between the sensor coordinate system and a marker coordinate system; determining a relation MA↔C between the MA coordinate system and the sensor coordinate system; determining a relation R↔E between the robot coordinate system and a reference location coordinate system. The method also includes determining the relation R↔MA using the relation C↔M, the relation MA↔C, and the relation R↔E, in the plurality of different poses.