Container Opening Recognition for Precise Robot Pick-and-Place
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Solution Overview
Problem
Existing recognition techniques for the position of articles in containers are inadequate for precise robot operation, particularly in distinguishing and controlling the opening shapes of containers for efficient pick-and-place tasks.
Innovation Solution
A processing device equipped with a recognition unit that utilizes a camera to capture and analyze container openings, employing machine learning and neural networks for instance segmentation to identify and differentiate between container openings, enabling precise robot control for pick-and-place operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recognition techniques are used for article position in containers, then basic position detection is possible, but precise recognition of container opening shapes for robot operation cannot be achieved
Solution Approach 1:
The patent replaces conventional mechanical or simple optical recognition systems with a camera-based imaging system combined with neural network processing. The recognition unit uses a camera to capture images of container openings and employs neural networks to analyze and distinguish opening shapes, enabling precise recognition without complex mechanical measurement devices.
Solution Approach 2:
The patent changes the approach from direct measurement to image-based recognition by capturing visual parameters of container openings. The system captures images, extracts features such as opening shapes and positions, and uses these visual parameters for robot control, achieving high precision through parameter transformation from physical space to image space.
2Adaptability or versatility
If simple recognition methods are used, then processing speed is maintained, but the ability to distinguish and control different container opening shapes is insufficient
Solution Approach 1:
The patent segments the recognition task into distinct components: capturing images of container openings, extracting features to identify opening shapes and positions, and using these segmented recognition results to control robot operations. This segmentation enables the system to handle multiple container types and opening shapes efficiently.
Solution Approach 2:
The system performs preliminary recognition of container opening shapes and positions before robot operations begin. By pre-processing and identifying container characteristics in advance, the robot can execute pick-and-place operations more efficiently without real-time decision delays.
3Measurement precision
If high-precision camera imaging and neural network recognition are implemented, then opening shape recognition accuracy is improved, but computational load and processing time increase
Solution Approach 1:
The recognition unit performs image capture and neural network processing in advance before robot operations are executed. By completing the recognition of opening shapes and positions beforehand, the system reduces real-time processing requirements and enables faster robot response during actual operations.
Solution Approach 2:
The system creates a digital representation (image copy) of the container opening that is then processed by the neural network. This copying approach allows the physical container to remain unchanged while its visual characteristics are analyzed, enabling rapid recognition without physical manipulation or additional measurement time.
Data Source
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AI summary
A processing device includes a recognition unit that obtains a camera image of an opening of a container and recognizes an opening shape of the opening based on the obtained camera image. The container receives an operation target object for a robot.