Co-Located Sorting Sensors for Automated Object Classification
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Solution Overview
Problem
Sorting facilities face challenges in dynamically reconfiguring their machines to optimize performance in response to events like jams or changes in material streams, requiring laborious manual adjustments and lacking efficient automation.
Innovation Solution
A cloud-based management control system that collects feedback data from sorting facility devices to dynamically reconfigure their positions and parameters, allowing for optimal sorting performance without physical modifications, using machine learning models and networked sensors to adjust sorting lines and bale composition in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual retuning of machines is performed to optimize performance, then sorting accuracy can be improved, but labor time and operational complexity increase significantly
Solution Approach 1:
The sorting facility performs self-optimization by automatically detecting performance deviations and adjusting machine parameters without human intervention. The system monitors sorting accuracy metrics and autonomously retunes machines in response to detected jams or performance degradation, eliminating the need for manual retuning while maintaining high sorting accuracy.
Solution Approach 2:
A feedback loop continuously monitors sorting performance and machine operation status, then automatically adjusts machine parameters to optimize sorting accuracy. The system uses real-time data from sensors and performance metrics to dynamically retune machines, replacing manual adjustment processes with automated closed-loop control.
2Adaptability or versatility
If dynamic reconfiguration of sorting facility is implemented, then adaptability to events like jams improves, but system complexity and control requirements increase
Solution Approach 1:
The sorting facility transitions from static machine configurations to dynamic reconfiguration, where machine parameters and sorting line settings automatically adjust in response to detected jams or external circumstances. This enables the system to adapt to changing conditions without requiring complex manual intervention or overly sophisticated control architectures.
Solution Approach 2:
The system autonomously detects performance deviations caused by jams or external events and automatically reconfigures sorting machines and conveyor lines to maintain optimal operation. This self-service capability provides adaptability to disturbances while keeping control system complexity manageable through rule-based or machine learning-driven automated responses.
3Measurement precision
If real-time data collection and analysis is performed, then sorting optimization accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system pre-processes and analyzes sorting data in real-time as it is generated, immediately identifying performance deviations and triggering optimization actions without significant delay. By performing data analysis concurrently with sorting operations and using predictive algorithms, the system achieves high optimization accuracy while minimizing data processing time and avoiding batch processing delays.
Data Source
AI summary
Co-located sensors in a sorting facility are disclosed, including: a first sensor associated with a first field of view, and wherein the first sensor is located in a sorting facility; a second sensor associated with a second field of view, wherein the second field of view is adjacent to or overlaps with the first field of view, and wherein the second sensor is located in the sorting facility; and one or more processors configured to: receive a first image including a first set of objects from the first sensor; receive a second image including a second set of objects from the second sensor; and evaluate the first image and the second image to determine a sorting decision with respect to a target object.