Layer Picker Vision Control for False Alarm Continuity
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Solution Overview
Problem
Existing material handling apparatuses, particularly layer pickers, experience inefficiencies due to false positive alarm signals, which require manual intervention to resume the handling process, leading to increased downtime and reduced productivity.
Innovation Solution
A computer-implemented method using a machine-learning model to analyze recorded image data from cameras and determine if an alert signal indicating an imminent interruption of the handling process is a false positive, generating a control signal to continue the handling process if deemed so.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a light sensor is used to detect packaging material to ensure safety, then reliability is improved, but false positive alarm signals increase causing unnecessary process interruptions
Solution Approach 1:
A machine learning model serves as an intermediary between the light sensor detection and the alarm signal generation. The model analyzes images to distinguish between genuine safety threats (dropped items) and benign conditions (hanging packaging material), filtering out false positives before they interrupt the process.
Solution Approach 2:
The patent replaces the simple mechanical/optical light sensor alarm system with an intelligent vision-based system using machine learning. This substitution enables nuanced interpretation of sensor data, allowing the system to differentiate between actual hazards and normal variations in packaging appearance.
2Reliability
If manual checking is required for alarm signals to ensure safety, then reliability is improved, but loss of time increases due to hot downtimes
Solution Approach 1:
The system performs self-verification by using the machine learning model to automatically assess alarm conditions and determine whether they represent genuine threats. This eliminates the need for manual intervention in false positive cases, allowing the system to clear its own alarms and continue operation without human involvement.
Solution Approach 2:
The machine learning model performs preliminary analysis of potential alarm conditions before they trigger process interruptions. By pre-evaluating the nature of detected anomalies, the system can distinguish between threats requiring manual verification and benign conditions that can be automatically dismissed, preventing unnecessary downtime.
3Reliability
If traditional alarm systems are used to detect potential dropping, then safety is maintained, but productivity decreases due to frequent process interruptions
Solution Approach 1:
The patent replaces traditional mechanical/optical alarm systems with an intelligent machine learning-based vision system. This substitution maintains comprehensive safety monitoring while adding cognitive capabilities to interpret sensor data, reducing false alarms and maintaining process continuity without compromising safety detection.
Solution Approach 2:
The system changes the operational parameters of the detection system by introducing probabilistic decision-making through machine learning. Instead of fixed threshold-based alarm triggers, the system uses learned patterns and probabilities to dynamically assess threats, allowing for more nuanced responses that maintain safety while reducing unnecessary interruptions.
Data Source
AI summary
The present disclosure relates to a computer-implemented method for operating a material handling apparatus, comprising: while the material handling apparatus is performing a handling process, receiving an alert signal indicating an imminent interruption of the handling process; analyzing, using a machine-learning model, recorded image data of the handling process; determining, using the machine-learning model, that the alert signal is a false positive; and generating a control signal for instructing the material handling apparatus to continue the handling process.