Pallet Sled Camera Vision for Inventory Verification
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Solution Overview
Problem
Current delivery systems lack efficient methods for verifying pallet contents and inventory levels during delivery, leading to potential errors in product distribution and inventory management.
Innovation Solution
A pallet sled equipped with a camera and computer system that analyzes images to identify SKUs, determine inventory levels, and assess store conditions, along with sensors for tracking and validation, ensures accurate delivery and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of pallet contents and inventory levels is performed, then delivery accuracy can be ensured, but time consumption and labor costs increase
Solution Approach 1:
The patent replaces manual visual inspection and physical counting with an automated image recognition system using cameras and computer vision algorithms. The system captures images of pallets and store shelves, then automatically identifies SKUs, counts inventory levels, and verifies delivery contents without human intervention, thereby maintaining verification accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-verification by equipping the pallet sled with onboard cameras and processing capabilities. The sled autonomously captures images during delivery operations and automatically analyzes inventory contents, eliminating the need for external verification personnel and enabling real-time self-monitoring of delivery accuracy.
2Measurement precision
If detailed inventory tracking is implemented, then inventory management accuracy improves, but system complexity increases
Solution Approach 1:
The patent integrates multiple functions into a single unified system: the camera captures images, the image recognition algorithm identifies SKUs and counts items, the accelerometer detects delivery events, and the system communicates with central inventory databases. This multi-functional integration achieves detailed inventory tracking while minimizing additional system complexity by consolidating capabilities into one platform.
Solution Approach 2:
The system uses image recognition technology as an intermediary between physical inventory and digital record-keeping. Instead of directly interacting with physical goods, the system captures optical images and uses AI algorithms to extract inventory information, creating a non-intrusive bridge between the physical and digital realms that simplifies the tracking process.
3Reliability
If continuous monitoring of delivery processes is performed, then delivery condition assessment improves, but energy consumption increases
Solution Approach 1:
The system employs periodic monitoring rather than continuous operation. The camera and sensors are activated at key moments during delivery - when pallets are loaded, during transport, and upon arrival - to capture critical condition data. This periodic action maintains delivery condition assessment reliability while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The system performs preliminary condition assessments by capturing baseline images and data before delivery operations begin. This preliminary action establishes reference conditions that enable later comparisons without requiring continuous monitoring, thereby maintaining reliability while reducing ongoing energy requirements.
Data Source
AI summary
A pallet sled includes a pair of tines extending forward from a base. A camera is mounted on the pallet sled and is configured to record images, such as video, as the pallet sled brings pallets of items from a truck into a store. At least one processor is used to analyze the images from the pallet sled. The images are used to determine inventory levels in the store, conditions of the store, conditions of the pathways of the pallet sled. An accelerometer on the pallet sled detects hard braking and accidents.