Robot Vision HSV Recognition for Autonomous Product Grasping
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Solution Overview
Problem
In smart factory environments, product detection based on HSV color space and robot machine vision requires human intervention and presets, making it difficult to accurately extract product information due to environmental factors like noise, light reflection, and lighting interference.
Innovation Solution
A method and apparatus for automatically recognizing the location of an object using the HSV color space, which involves obtaining image data, detecting location and shape information, generating a recipe for robot grasping and transfer, and transmitting this recipe to the robot, all without human intervention or prior knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If HSV color space and robot machine vision are used for product detection, then automation is improved, but human intervention and presets are still required
Solution Approach 1:
The system performs self-learning by automatically capturing product images, extracting color information from multiple ROIs, and generating HSV threshold values without human intervention. The robot also performs self-teaching by autonomously learning grasp positions and motion parameters through repeated operations, eliminating the need for manual programming
Solution Approach 2:
The system performs preliminary actions by pre-capturing product images under various lighting conditions, pre-extracting color information from multiple ROIs, and pre-generating threshold values before actual production. This preliminary data collection enables the system to adapt to different products and environments without requiring human setup
2Measurement precision
If HSV color space is used for product detection, then color recognition is improved, but accuracy is reduced due to environmental factors like noise, light reflection, and lighting interference
Solution Approach 1:
The system segments the product image into multiple ROIs (Regions of Interest) and extracts color information from each ROI independently. By dividing the product into multiple sampling regions, the system obtains more comprehensive color data that is less susceptible to localized environmental interference such as reflections or shadows
Solution Approach 2:
The system dynamically adjusts HSV threshold values based on actual product color measurements rather than using fixed predetermined values. By changing the parameters from static to adaptive, the system maintains accurate color recognition despite variations in lighting conditions, camera angles, and environmental factors
3Ease of operation
If manual robot teaching is performed for product transfer and picking, then robot operation is improved, but time and effort consumption increase
Solution Approach 1:
The robot performs self-teaching by autonomously learning grasp positions, motion trajectories, and operation parameters through automated image processing and data extraction. The system eliminates manual programming by having the robot learn from captured product images and automatically generate control parameters
Solution Approach 2:
The system replaces manual mechanical teaching operations with automated computational processes. Instead of manually positioning and programming the robot, the system uses image processing algorithms, color space analysis, and automated parameter generation to determine robot motion and grasping parameters
Data Source
AI summary
A method for automatically recognizing a location of an object so that a robot can grasp and transfer the product in an apparatus for automatically recognizing a location of an object is provided. The method for automatically recognizing a location of an object obtains one frame from image data of the product produced by the manufacturing facility, detects location and shape information of the product based on a hue, saturation, value (HSV) color space using the frame, generates a recipe for the robot to grasp and transfer the product based on the location and shape information of the product, and transmits the recipe to the robot.