Inventory Weight Sensor Accuracy via Camera Fusion
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Solution Overview
Problem
Existing inventory management systems face challenges in accurately tracking interactions and maintaining reliable data due to noise in weight sensors, especially from vibrations and the presence of similar items with different weights, leading to erroneous determinations.
Innovation Solution
The use of weight sensors in conjunction with non-weight data from cameras and optical sensor arrays to process interaction data, where non-weight data helps in selecting the correct hypothesis about item identity and quantity picked or placed, and in determining the reliability of weight data by identifying activity at the inventory location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If weight sensors are used to track inventory interactions, then automation and tracking capability are improved, but measurement precision deteriorates due to noise from vibrations and similar items with different weights
Solution Approach 1:
The patent combines weight sensor data with non-weight sensor data (cameras, optical sensors) to track item interactions. By merging multiple data sources, the system overcomes the limitations of weight sensors alone, which cannot distinguish between similar items with different weights or filter out vibration noise effectively.
Solution Approach 2:
The patent introduces non-weight sensors as intermediary detection means to validate and supplement weight sensor data. These additional sensors act as mediators that help distinguish actual item interactions from noise, improving measurement precision while maintaining automation.
2Device complexity
If only weight sensors are used, then device complexity is reduced, but reliability deteriorates due to erroneous determinations from noise and similar items
Solution Approach 1:
The system merges weight sensors with non-weight sensors (cameras, optical sensors) to create a multi-modal detection system. This combination improves reliability by cross-validating data from different sensor types, reducing erroneous determinations caused by noise or similar items.
Solution Approach 2:
The system uses feedback from multiple sensor sources to validate weight sensor readings. When weight data is ambiguous or noisy, feedback from camera and optical sensor data helps confirm or correct the interpretation, improving overall system reliability.
3Productivity
If weight sensors are used to monitor inventory, then productivity is improved through automated tracking, but measurement precision worsens due to inability to distinguish similar items with different weights
Solution Approach 1:
The patent merges weight-based detection with visual detection from cameras and optical sensors. This combination maintains the productivity benefits of automated weight-based tracking while adding visual confirmation capability to distinguish between similar items with different weights, shapes, or packaging.
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 approach improves the accuracy of tracking interactions and maintaining up-to-date inventory levels by reducing errors caused by noise, ensuring reliable data and efficient operation in materials handling facilities.
Implementation Method 1
Weight sensors may be used to gather weight data about items stowed at inventory locations
Implementation Method 2
non-weight data from cameras and optical sensor arrays
Data Source
AI summary
An inventory location such as a shelf may be used to stow different types of items, with each type of item in a different partitioned area or section of the shelf. Weight data from weight sensors coupled to the shelf is used to determine a change in weight of the shelf and a change in the center-of-mass (“COM”) of the items on the shelf. Based on the weight data and item data indicative of what items are stowed in particular partitioned areas, activity such as a pick or place of an item and the partitioned area in which the activity occurred may be determined. Data from other sensors, such as a camera, may be used to confirm the occurrence of the activity, disambiguate the determination of the particular partitioned area, and so forth.