Mobile Camera Unit for Pallet SKU Validation
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Solution Overview
Problem
The process of delivering products from distribution centers to stores is prone to errors and inefficiencies due to missing or mis-picked items when building pallets, leading to significant additional operating costs.
Innovation Solution
A mobile camera unit equipped with cameras and a processor is used to image products on a pallet, with images analyzed using machine learning to identify SKUs and compare them to the order list, allowing for real-time correction of missing or incorrect items before shipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual pallet building is used, then operational flexibility is maintained, but error rate increases and productivity decreases
Solution Approach 1:
The system enables self-service validation where the camera unit automatically captures images of the built pallet, the machine learning model autonomously identifies SKUs and compares them against the pick list, and the system self-corrects by alerting operators to discrepancies without requiring manual verification steps
Solution Approach 2:
The patent replaces manual visual inspection and verification processes with an automated optical system. The camera unit captures images, machine learning algorithms analyze the visual data to identify products, and automated comparison logic validates the pallet contents against the order, substituting human cognitive tasks with computational processes
2Measurement precision
If automated validation system is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The camera unit serves as an intermediary that captures visual data of the pallet contents, which is then transmitted to a remote server or cloud-based machine learning model for analysis. This intermediary approach allows the validation system to achieve high measurement precision without requiring complex processing hardware at the pallet building location
Solution Approach 2:
The system creates a visual copy (image) of the physical pallet contents, which is then analyzed by machine learning algorithms to identify SKUs. This copying approach simplifies the physical system while maintaining high identification accuracy, as the complex analysis is performed on digital representations rather than requiring direct interaction with physical products
3Measurement precision
If multiple cameras are used to image all sides of pallet, then measurement completeness improves, but loss of time increases
Solution Approach 1:
The system uses multiple cameras positioned at different angles to capture images of the pallet from multiple perspectives simultaneously. By capturing excessive visual information from all sides, the system ensures complete product verification while the parallel capture approach minimizes the time penalty compared to sequential imaging
Data Source
AI summary
A mobile camera unit includes a base having a plurality of wheels. A support extends upward from the base. At least one camera mounted to the support. At least one processor is configured to collect images from the at least one camera. A communication circuit is configured to transmit the images. The mobile camera unit may be used to capture images of a plurality of products on a pallet or other platform. The images are transmitted to a server to identify SKUs associated with the products and to compare the identified SKUs with an order or pick list.