Load Cell Compensation Model for Mobile Cart Weight Accuracy
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Solution Overview
Problem
Mobile apparatuses, such as smart shopping carts, face challenges in maintaining accurate weight measurements due to temperature, humidity, and localization errors, which are not adequately addressed by existing calibration methods, particularly for NTEP and OIML certifications.
Innovation Solution
A compensation model is developed using a statistical data modeling process that predicts and corrects for these errors by training on a subset of mobile apparatuses, applying a correction factor based on environmental conditions and load location, using Ordinary Least Squares regression to ensure accuracy across a fleet of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used for load cells, then initial weight measurement accuracy is achieved, but measurement precision deteriorates under varying environmental conditions (temperature, humidity) and load locations
Solution Approach 1:
The system changes the parameters used for compensation by measuring multiple environmental parameters (temperature, humidity) and using them to dynamically adjust weight measurements. The compensation model uses these parameter changes to correct for environmental effects on load cell readings, maintaining accuracy across varying conditions without requiring physical recalibration.
2Measurement precision
If exhaustive recalibration is performed for all environmental conditions and load locations, then measurement precision is maintained, but device complexity and calibration time increase significantly
Solution Approach 1:
The system introduces an intermediary compensation model that acts as a mediator between the load cell measurements and the final weight reading. This model processes environmental parameters and load location data to generate correction factors, eliminating the need for complex exhaustive recalibration while maintaining measurement precision.
Solution Approach 2:
The system performs preliminary measurements of environmental conditions (temperature, humidity) and load location before final weight calculation. This preliminary action allows the compensation model to pre-determine appropriate correction factors, avoiding the need for complex real-time recalibration during operation.
3Measurement precision
If compensation models are trained on every individual mobile apparatus, then measurement precision is optimized for each device, but loss of time and calibration resources increase
Solution Approach 1:
The system creates a universal compensation model that can be applied across multiple mobile apparatuses (shopping carts). Instead of training separate models for each device, the same compensation model serves multiple devices, reducing calibration time and resources while maintaining sufficient measurement precision through environmental parameter-based adjustments.
Data Source
AI summary
This disclosure describes, in part, systems and techniques for determining compensation models to adjust load cell data and ensure accuracy of the load cells on a mobile apparatus through changing environments and conditions. This disclosure relates, specifically, to generating a compensation model by gathering sensor data for known weights of items over a range of weights, locations, temperatures, and humidity values and building a compensation model to infer compensation error to apply to the estimated weight data from the load cells. The compensation model can be used by a fleet of carts to infer weights of items in a manner accurate enough for sale-by-weight of items.