Goods Picking Assembly With Sensor-Based Container Depth Measurement
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Solution Overview
Problem
Existing intelligent warehousing systems face issues with the safe placement of goods containers, as they often rely on default values for retrieval and placement, leading to potential damage or falling of containers due to mismatched sizes and distances.
Innovation Solution
A goods picking apparatus equipped with sensors and a depth determining module that measures the depth of a goods container and adjusts the picking and placement process accordingly, using through-beam sensors, visual sensors, or distance measurement sensors to determine the optimal placement depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple types of goods are stored in the same storage space, then storage space utilization is improved, but the risk of damaging vulnerable goods during extraction increases
Solution Approach 1:
The system applies different extraction forces based on the specific characteristics of each good being extracted. The control unit determines the type of good (fragile, soft, hard, heavy) and adjusts the extraction force parameters accordingly, allowing each extraction action to be optimized for its specific target while operating in a mixed storage environment.
Solution Approach 2:
The system changes the extraction force parameters dynamically based on the good type being extracted. By adjusting force magnitude, velocity, and acceleration parameters according to the detected good characteristics, the system can safely extract vulnerable goods from shared storage spaces without causing damage.
2Productivity
If extraction force is increased to extract goods firmly attached to shelves, then extraction efficiency is improved, but the risk of damaging vulnerable goods increases
Solution Approach 1:
The extraction force is not static but dynamically adjusted based on real-time detection of good characteristics. The system transitions from fixed-force extraction to adaptive-force extraction, where the force parameters are modified during operation based on the detected good type and attachment state.
Solution Approach 2:
The system changes extraction force parameters (magnitude, velocity, acceleration) based on the detected good type. For firmly attached goods, higher forces are applied; for vulnerable goods, lower forces are used, optimizing both extraction efficiency and damage prevention.
3Measurement precision
If manual inspection of each good is performed to determine extraction method, then extraction accuracy is improved, but processing time increases
Solution Approach 1:
The system replaces manual visual inspection with automated image recognition technology. The imaging device captures images of goods on shelves, and the control unit automatically analyzes these images to identify good types and determine appropriate extraction methods, eliminating the need for manual inspection while maintaining high accuracy.
Solution Approach 2:
The system creates a digital representation (image) of the physical goods and processes this copy to determine extraction parameters. By working with the image data rather than physically inspecting each good, the system achieves rapid automated classification and extraction planning.
4Ease of operation
If storage spaces are dedicated to specific good types, then good management is improved, but storage space utilization decreases
Solution Approach 1:
The extraction device is designed to handle multiple types of goods (fragile, soft, hard, heavy) using the same storage space. The system's ability to automatically detect and adapt to different good types makes the storage system universal, allowing any storage location to accommodate various good types without requiring dedicated spaces for each category.
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
This solution enhances the safety and efficiency of container placement, reducing the likelihood of damage and falling, and improves the intelligence and operational efficiency of warehousing robots by adapting to varying container sizes.
Implementation Method 1
an imaging device to capture images of goods on a shelf
Implementation Method 2
a control unit to control the moving device, the imaging device and the extraction device, where the control unit is configured to calculate, based on the captured images, a depth of the good relative to the shelf
Data Source
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AI summary
Embodiments of the present disclosure provide a goods picking apparatus, a depth measurement method, a warehousing robot, and a warehousing system. The goods picking apparatus includes a goods picking assembly, a sensor, and a depth determining module; the sensor is provided on the goods picking assembly and configured to collect a measurement signal when the goods picking assembly extends; and the depth determining module is configured to determine a depth of a goods container according to the measurement signal to pick and/or place the goods container according to the depth of the goods container, where the depth is a length of the goods container in an extension direction of the goods picking assembly during goods picking. Through the sensor and the depth determining module arranged on the goods picking assembly, the depth of the goods container is measured, and the efficiency of goods container retrieval and the safety of goods container placement are improved.