Multi-Sensor Resource Tracking for Accurate Pallet Quantification
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Solution Overview
Problem
Conventional inventory management systems, particularly in e-commerce, face challenges with labor-intensive pallet building processes that are error-prone, leading to inaccurate resource deliveries, increased costs, and safety hazards due to manual counting and tracking of resources on pallets.
Innovation Solution
A multi-sensor perception system using computing devices equipped with imaging devices and LIDAR sensors to track and quantify resources, employing machine learning models for accurate identification, tracking, and placement, eliminating the need for manual scanning and logging, and providing real-time notifications for discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting and tracking of resources is used, then labor flexibility is maintained, but accuracy of resource tracking deteriorates
Solution Approach 1:
The patent replaces manual mechanical counting and tracking with an automated imaging system using cameras and LIDAR sensors. The system captures images of resources on pallets, uses machine learning models to identify and count resources automatically, eliminating the need for manual scanning and logging while significantly improving tracking accuracy.
Solution Approach 2:
The system creates digital copies of physical resources through imaging. Cameras capture visual images and LIDAR sensors create depth maps of resources, which are then processed by machine learning models to generate accurate counts and identification data, replacing the need for physical manual counting.
2Productivity
If manual pallet building is used, then operational flexibility is maintained, but productivity deteriorates
Solution Approach 1:
The imaging system operates continuously during the pallet building process, capturing images at multiple stages without interrupting workflow. The system takes images during resource placement, processes them through machine learning models, and provides real-time feedback, eliminating the need for separate manual counting steps and maintaining continuous productive action.
Solution Approach 2:
The system performs resource identification and counting automatically as resources are placed on pallets, before the pallet is finalized and shipped. This preliminary automated tracking prevents errors from being discovered later and eliminates the need for post-palleting verification, saving significant time.
3Reliability
If manual resource placement is used, then adaptability to different pallet configurations is maintained, but reliability of resource accuracy deteriorates
Solution Approach 1:
The system provides real-time feedback by comparing actual resource placement against the purchase order requirements. The machine learning model identifies resources, counts them, and verifies their correct placement on the pallet. If discrepancies are detected, the system generates notifications for immediate correction, ensuring high reliability of pallet composition and preventing safety hazards from improper packing.
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
Significantly improves the accuracy of pallet building, reduces labor intensity, minimizes errors, and enhances safety by enabling precise resource tracking and quantification, thereby reducing lost revenue and improving operational efficiency.
Implementation Method 1
A multi-sensor perception system using computing devices equipped with imaging devices and LIDAR sensors to track and quantify resources
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for multi-sensor perception for resource tracking and quantification. An embodiment operates by receiving resource information indicating a resource identifier, a resource location, and a resource amount. Based on sensor data received from a first sensing device and the resource identifier, a resource removed from the resource location and placed at a predefined location may be tracked. An amount of the resource placed at the predefined location may be determined based on depth information from sensor data received from a second sensing device. A notification that indicates that additional resources that match the resource should stop being removed from the resource location and placed at the predefined location may be generated based on a match between the resource amount and the amount of the resource placed at the predefined location.