Multi-Frame Robotic Calibration via Constraint Pairs
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Solution Overview
Problem
Current robotic systems face challenges in maintaining accurate calibration of robots and sensors in an operating environment, leading to errors that can accumulate and result in failed tasks.
Innovation Solution
A system that utilizes multiple calibration results and external measurements across different coordinate frames to optimize calibration, by defining constraint pairs that specify pose relationships and calculating local and global errors to adjust poses of sensors and robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hand-eye calibration is used to optimize calibration in an operating environment, then calibration accuracy can be improved, but the system cannot account for accumulated errors from multiple coordinate frames and physical movements
Solution Approach 1:
The patent segments the calibration problem into multiple independent constraint pairs, each representing a specific relationship between coordinate frames. Instead of performing a single comprehensive calibration, the system divides the calibration into smaller units (constraint pairs) that can be optimized independently and then combined, allowing error propagation to be managed at each segment rather than accumulating across the entire system.
Solution Approach 2:
The patent introduces a new dimension to calibration by incorporating external measurements from multiple sensors observing the same physical entity. This multi-sensor, multi-coordinate frame approach adds dimensional complexity to the calibration process, enabling the system to cross-validate and resolve discrepancies that a single calibration path cannot detect.
2Manufacturing precision
If multiple sensors and robots are positioned exactly as expected in a CAD model, then task execution accuracy is improved, but real-world installation and calibration errors cause deviations from expected poses
Solution Approach 1:
The system performs self-calibration by automatically comparing measurements from multiple sensors and constraint pairs, identifying discrepancies, and adjusting poses without requiring manual intervention or complex installation procedures. The calibration process serves itself by using the existing sensor network and constraint relationships to detect and correct errors.
Solution Approach 2:
The patent changes the calibration parameters from fixed CAD model coordinates to dynamically adjusted poses based on actual sensor measurements. By allowing pose parameters to be modified based on observed discrepancies across multiple constraint pairs, the system adapts the theoretical CAD positions to real-world conditions while maintaining task accuracy.
3Measurement precision
If all local errors are solved for in the calibration process, then complete calibration accuracy is achieved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The system applies partial action by solving only the most significant constraint pairs rather than attempting to resolve every possible local error simultaneously. By selecting a subset of critical constraint pairs that have the greatest impact on overall calibration accuracy, the system achieves sufficient precision without the computational burden of exhaustive error correction.
Solution Approach 2:
The patent performs preliminary action by pre-defining constraint pairs and their relationships in the calibration model before actual calibration execution. This pre-structuring of calibration relationships allows the system to efficiently process measurements during operation, as the computational framework is already established and does not require real-time complex decision-making about which errors to correct.
4Adaptability or versatility
If robots and sensors are physically moved while performing tasks, then operational flexibility is improved, but calibration errors accumulate particularly when components are moved far from their base positions
Solution Approach 1:
The system implements feedback by continuously monitoring pose discrepancies across multiple constraint pairs and using this information to adjust calibration parameters. When robots or sensors are physically moved, the multi-constraint calibration framework detects the resulting errors through sensor measurements and provides corrective feedback to maintain accuracy, allowing flexible repositioning without permanent calibration degradation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for calibrating a robotic workcell. One of the methods includes obtaining an initial model of a workcell having a plurality of calibration entities including a plurality of robots and a plurality of sensors configured to observe movements by one or more calibration entities. executing a calibration program that generates movement data representing movements by the plurality of robots. A plurality of different constraint pairs are generated from sensor data, the constraint pairs specifying a relationship between poses of calibration entities that are observed in different coordinate frames each defined by a calibration entity and is represented in the sensor data. One or more optimization processes are performed on the plurality of different constraint pairs to generate a plurality of calibration values.