Robot Vision Positioning Using Determination Points and Threshold Conversion
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Solution Overview
Problem
Current robot control methods require manual setting of thresholds for positional accuracy, which is time-consuming and labor-intensive, especially when precise tilt angles are needed, and are prone to errors due to the complexity of calculating circularity and vision sensor errors.
Innovation Solution
A method involving a vision sensor to measure feature points, convert positional differences, and compare them with thresholds to determine the robot's posture, using a determination point for quick convergence and reduced worker workload, with a controller calculating transformation parameters to correct the robot's posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a worker determines whether the tilt of a leading end of a robot converges by calculating the circularity of a mark, then the positioning accuracy of the robot is improved, but the workload and time required for setting thresholds is increased
Solution Approach 1:
The patent introduces a conversion unit that converts the circularity value (measured by the vision sensor) into an equivalent translational distance value. This intermediary conversion allows the system to use simple translational thresholds instead of requiring workers to understand and set complex circularity thresholds, thereby reducing workload while maintaining positioning accuracy.
Solution Approach 2:
The patent changes the parameter being monitored from circularity (which requires complex interpretation) to translational distance (which is intuitive and easy to threshold). By converting the measurement parameter into a more manageable form, the system achieves high positioning accuracy without requiring extensive worker expertise or time for threshold setting.
2Measurement precision
If a worker sets thresholds for circularity and positional difference, then the robot positioning accuracy is improved, but the complexity and difficulty of the operation is increased
Solution Approach 1:
The conversion unit acts as an intermediary that translates complex circularity measurements into simple translational distance equivalents. This allows workers to set thresholds based on intuitive distance values rather than having to understand the complex relationship between camera tilt and circularity changes, significantly improving ease of operation.
Solution Approach 2:
The patent replaces the need for manual calculation and understanding of circularity mechanics with an automated conversion process. The conversion unit automatically performs the complex calculations and provides converted values that are straightforward to threshold, substituting complex mechanical understanding with automated computational processing.
3Measurement precision
If the robot performs repetitive motions to correct positional differences, then the positioning accuracy is improved, but the time required for convergence is increased
Solution Approach 1:
The patent implements a feedback mechanism where the vision sensor continuously monitors the mark's position and circularity, the conversion unit converts these measurements into translational distance equivalents, and the robot adjusts its position based on this feedback. This closed-loop feedback system enables rapid convergence to the target position while maintaining high accuracy.
Solution Approach 2:
The conversion unit performs preliminary conversion of circularity values to translational distance equivalents before the robot executes corrective motions. This preliminary processing provides the robot with immediately actionable information in a format that enables rapid response, reducing the time required for convergence while maintaining positioning accuracy.
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3C
AI summary
A robot system (100) includes a robot (200), a vision sensor (300), a controller (500,600), and an input unit (850) . The vision sensor (300) configured to measure a feature point (MK1, MK2, MK3) and obtain a measured coordinate value. The controller (500,600) configured to control the robot (200). The input unit (850) configured to receive an input from a user toward the controller (600). The controller (600) obtains, via the input unit (850), setting information data on a determination point which is different from the feature point (MK1, MK2, MK3). The robot system uses a coordinate value of the determination point and the measured coordinate value, and determines whether the robot is taking a target position and orientation.