Forklift Camera System for Automated Bottle Identification
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Solution Overview
Problem
The existing methods for unloading and identifying returnable bottles are error-prone due to time constraints, multiple pallet handling, confusion between different crate/bottle types, poor visibility, and manual data entry, leading to stock discrepancies and production halts in the beverage industry, resulting in high costs and resource waste.
Innovation Solution
A forklift truck equipped with a first and second camera system, sensor systems for distance detection, and a data processing system that captures and analyzes image data in real-time to accurately identify the type and quantity of bottles, ensuring precise stock recording and efficient production processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording of crate/bottle type is used by forklift drivers, then the system is simple to operate, but the error rate increases significantly
Solution Approach 1:
The patent replaces the mechanical manual input system with an automated optical recognition system. Cameras capture images of crates and bottles, and image processing algorithms automatically identify and record the crate/bottle types, eliminating manual data entry and significantly reducing errors.
Solution Approach 2:
The system creates visual copies (images) of the physical crates and bottles using cameras. These image copies are then processed to extract identification information, replacing the need for manual observation and recording by the forklift driver.
2Productivity
If multiple forklifts unload simultaneously to increase speed, then productivity improves, but visibility and recording accuracy deteriorate
Solution Approach 1:
The system enables each forklift to independently perform identification and recording of its own load without requiring centralized coordination or clear visibility of other forklifts. The onboard cameras and processing system allow parallel operation of multiple forklifts while maintaining accurate recording.
3Productivity
If tight time limits are imposed on unloading, then productivity increases, but error rate increases
Solution Approach 1:
The automated recognition system operates continuously during the unloading process without interruption. Cameras continuously capture images and the processing system continuously identifies crates and bottles, eliminating the need for pause-and-record operations that would slow down unloading while maintaining accuracy.
4Adaptability or versatility
If manual input menus contain many product types, then the system can handle diverse products, but the ease of operation decreases
Solution Approach 1:
The system extracts the identification task from the operator's cognitive load and transfers it to the automated image processing system. The complex task of distinguishing between many product types is removed from manual operation and handled by algorithms that analyze visual features of crates and bottles.
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
The solution enables accurate and efficient detection of bottle types and quantities, reducing errors and stock discrepancies, thereby improving production efficiency and minimizing resource waste and costs.
Implementation Method 1
a first sensor system with a first sensor configured to detect the distance between the forklift truck and the product
Data Source
Figure 1A
Figure 1B
Figure 2A~2C
AI summary
According to one embodiment, a forklift truck (10) comprises: a first camera system with a first camera to capture a first side (S1) of a product (W) to be picked up; a second camera system with a second camera (14) to capture a second side of the product; a first sensor system with a sensor (16) to detect the distance of the forklift truck from the product; a controller (20) that outputs a first activation signal to the first camera when the distance detected by the sensor falls below a minimum distance; and a transmission unit (22) to transmit the image data of the product captured by the first and second cameras, respectively, to a data processing system (24).According to a further embodiment, the system comprises: a forklift (110) with a tracking system to determine the position of the forklift in a warehouse; a first camera system with a first camera (112) to capture a side of a product (W) facing the forklift; a sensor system with a sensor to detect the distance between the forklift and the product; a controller to output a first activation signal to the first camera when the distance detected by the first sensor falls below a minimum distance; and a transmission system to transmit the image data of the product captured by the first camera to a data processing system. The system includes a second camera system that captures a side of the product facing away from the forklift.According to the two embodiments, the data processing system analyzes the image data captured by the first camera and the second camera in order to identify the goods.