Robotic Cell Auto-Calibration Using Unified Vision and Pose Compensation
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Solution Overview
Problem
Current robotic assembly systems require time-consuming and expertise-dependent manual calibration, which is inefficient and limits their precision and accuracy.
Innovation Solution
An auto-calibration system that uses a holistic view of the robotic cell and its workspace, combining camera lens calibration, frame registration, end of arm tool contact calibration, and robot pose error compensation, to achieve high accuracy and precision with off-the-shelf components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration by experts is used, then calibration accuracy is achieved, but calibration time and complexity increase significantly
Solution Approach 1:
The robotic cell performs calibration autonomously using automated vision systems and self-diagnostic routines. The system captures images of fiducial markers, automatically computes transformation matrices, and adjusts its own positioning without human intervention, enabling the robot to calibrate itself while maintaining high accuracy.
Solution Approach 2:
Manual mechanical calibration operations are replaced with automated vision-based measurement systems and computational algorithms. The system uses cameras to detect fiducial markers and calculates calibration parameters through image processing and coordinate transformation, eliminating the need for manual mechanical adjustment.
2Measurement precision
If manual calibration by experts is used, then calibration accuracy is achieved, but operational complexity increases
Solution Approach 1:
The system automatically executes calibration routines, processes vision data, and applies calibration parameters without requiring expert operators. The automated workflow includes capturing fiducial images, computing transformation matrices, and updating robot positioning, all performed by the system itself.
Solution Approach 2:
The complex expert knowledge and manual judgment required for calibration are extracted and replaced with automated vision algorithms and computational models. The system extracts calibration information from fiducial marker images and automatically computes the necessary transformation parameters, removing the need for human expertise in the calibration process.
3Reliability
If multiple separate calibration routines are used for different components, then comprehensive calibration is achieved, but coordination difficulty and time increase
Solution Approach 1:
Multiple separate calibration routines for different components (robot arm, end effector, vision system) are merged into a unified calibration process. The system uses a single set of fiducial markers to simultaneously calibrate all components by computing a comprehensive transformation matrix that coordinates their relative positions and orientations.
Solution Approach 2:
The fiducial marker system serves multiple calibration functions simultaneously. The same markers are used to calibrate the robot arm positioning, end effector alignment, and vision system coordination, making the calibration process universal and eliminating the need for separate calibration routines for each component.
Data Source
AI summary
A robotic cell calibration method comprising a robotic cell system having elements comprising: one or more cameras, one or more sensors, components, and a robotic arm. The method comprises localizing positions of the one or more cameras and components relative to a position of the robotic arm using a common coordinate frame, moving the robotic arm in a movement pattern, and using the cameras and sensors to determine robotic arm position at multiple times during the movement. The method includes identifying a discrepancy in robotic arm position between a predicted position and the determined position in real time, and computing, by an auto-calibrator, a compensation for the identified discrepancy, the auto-calibrator solving for the elements in the robotic cell system as a system. The method includes modifying actions of the robotic arm in real time during the movement based on the compensation.