Wafer Transfer Robot Teaching Using LiDAR and Vision Coordinates
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Solution Overview
Problem
Existing methods for teaching wafer transfer positions in substrate treating apparatuses are prone to precision issues due to user proficiency and fatigue, and manual operations are time-consuming, with vision sensors having limitations in accurately determining three-dimensional coordinates.
Innovation Solution
A teaching method utilizing a combination of a lidar sensor for 3D position information, a vision sensor for 2D image-based coordinates, and a distance sensor to automatically derive precise transfer coordinates without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation method is used for teaching transfer robot, then operation flexibility is maintained, but teaching precision varies according to user proficiency and operation time is long
Solution Approach 1:
The patent replaces manual mechanical operation with an automated vision sensor system. The vision sensor automatically captures images of QR marks on the support mechanism, and the control apparatus automatically calculates teaching coordinates from these images, eliminating the need for manual operation while improving both precision and reducing time.
Solution Approach 2:
The system performs self-teaching by automatically capturing images and calculating coordinates without human intervention. The vision sensor and control apparatus work autonomously to complete the teaching process, making the system self-sufficient for this critical calibration task.
2Extent of automation
If vision sensor is installed on transfer robot to automate teaching, then manual operation time is reduced, but three-dimensional coordinate measurement capability is limited
Solution Approach 1:
The patent combines multiple sensor types (vision sensor for 2D coordinates and laser sensor for 3D depth measurement) into a unified teaching system. This integration allows the system to automatically acquire both 2D and 3D coordinates, achieving complete three-dimensional measurement capability while maintaining high automation.
Solution Approach 2:
The control apparatus acts as an intermediary that processes data from both vision and laser sensors. It integrates the 2D coordinate information from the vision sensor with the depth information from the laser sensor to calculate complete 3D teaching coordinates, bridging the capability gap between the two sensor types.
3Productivity
If vision sensor is used for teaching, then two-axis coordinates can be checked automatically, but third-axis distance information requires additional manual input
Solution Approach 1:
The control apparatus is designed with multi-functionality to handle both 2D image processing from the vision sensor and 3D coordinate calculation by integrating laser sensor data. This universal capability eliminates the need for separate manual input procedures, allowing the single device to complete the entire teaching process automatically.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances teaching operation precision and efficiency by automating the process, eliminating the need for manual operations and follow-up adjustments with wafer-type sensors.
Implementation Method 1
a first sensor which is a lidar sensor and acquires 3D position information
Implementation Method 2
a second sensor which is a vision sensor and acquires image data of the object
Implementation Method 3
a third sensor which is a distance sensor and acquires a linear distance between the transfer robot and the object
Data Source
AI summary
The inventive concept provides a teaching method for teaching a transfer position of a transfer robot. The teaching method includes: searching for an object on which a target object to be transferred by the transfer robot is placed, based on a 3D position information acquired by a first sensor; and acquiring coordinates of a second direction and coordinates of a third direction of the object based on a data acquired from a second sensor which is a different type from the first sensor.