Multi-Axis Robot Printhead Calibration by Optical Impact Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for calibrating multi-axis robots equipped with a camera and a printing head are complex and do not optimize calibration effectively, lacking a straightforward and accurate method for precise positioning of the printing head.
Innovation Solution
A method that determines the oriented position of the frame linked to the printing head by using a camera to measure the coordinates of impact points on a fixed surface, calculating deviations based on these coordinates, and minimizing an objective function to determine the six parameters of the passage matrix that define the printing head's position and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic verification and correction of print point position is performed before each operation cycle, then positioning accuracy is improved, but device complexity and calibration time increase
Solution Approach 1:
The calibration of the printing head position and orientation is performed once during the preliminary setup phase using the camera to capture impact points and calculate the passage matrix parameters. This preliminary calibration eliminates the need for repeated automatic verification and correction before each operation cycle, thereby reducing both calibration time and device complexity while maintaining positioning accuracy.
Solution Approach 2:
The camera captures optical images of the impact points on the target surface, creating a digital copy of the physical impact locations. These captured coordinates are then used to calculate the passage matrix parameters without requiring physical measurement tools or complex verification equipment, simplifying the calibration process while achieving high positioning accuracy.
2Measurement precision
If manual positioning and labeling methods are used for fine positioning, then positioning precision can be achieved, but ease of operation deteriorates due to complexity
Solution Approach 1:
The system performs self-calibration by automatically capturing images of impact points with the camera, calculating the coordinates, and determining the passage matrix parameters without requiring manual intervention or external labeling. The printing head itself creates the calibration marks through impact points, and the camera automatically records and processes this information, making the calibration process both precise and easy to operate.
Solution Approach 2:
The manual mechanical positioning and labeling process is replaced by an optical measurement system. The camera captures the positions of impact points optically, and computational algorithms calculate the passage matrix parameters, substituting complex manual mechanical adjustment operations with automated optical detection and mathematical computation, thereby improving ease of operation while maintaining fine positioning accuracy.
Data Source
AI summary
According to this method, a mathematical surface (PrefBF) is determined (104), a printing head is brought into a first position and then into a second position, where a first impact and a second impact are printed (106, 110), then the coordinates of a characteristic point (P1,4BF, P2,4BF) of the first or second impact (108, 112) are measured. We express (114) the coordinates of a first intersection point (I1,4BF) and those of a second intersection point (I2,4BF). We express (120) a deviation ({right arrow over (εk)}) based on the coordinates of the characteristic points (Pk,jBF) and intersection point (Ik,jBF). We construct (122) an objective function (F) whose variables are the deviations ({right arrow over (εk)}). We determine (124) values for six parameters (X1-X6) of a passage matrix (TTCP→PG) which minimise the objective function (F). These six parameters (X1-X6) are used (128) to define an oriented position of the frame (TCP) linked to the printing head in the frame linked to the wrist.