Predictive Conveyor Zone Mapping With Minimal Photo-Eyes
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Solution Overview
Problem
Conventional conveyor systems, particularly motor driven roller (MDR) systems, rely heavily on photo-eyes for zone control, leading to complex infrastructure, maintenance challenges, and operational inefficiencies due to the high dependency on photo-eye feedback.
Innovation Solution
A method and apparatus for predicting and mapping the real-time location of objects within a conveyor system using minimal photo-eye infrastructure by detecting an object's presence at an infeed point, determining its length, and configuring MDR rotational speeds based on this information, with verification at a discharge point to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If photo-eyes are used for zone control in conveyor systems, then object location tracking accuracy is improved, but infrastructure complexity and maintenance requirements increase
Solution Approach 1:
The patent extracts the core function of photo-eyes (detecting object presence and location) and replaces the physical infrastructure with a computational model. The virtual map system calculates object position based on conveyor zone information and predicted object movement, eliminating the need for actual photo-eyes in each zone while maintaining location tracking capability.
Solution Approach 2:
The patent creates a virtual copy of the physical conveyor zones and objects. Instead of physically detecting objects with photo-eyes in each zone, the system maintains a virtual representation (virtual map) that models object positions and movements, allowing control decisions to be made based on this virtual model rather than direct physical sensing.
2Measurement precision
If multiple photo-eyes are deployed throughout the conveyor system, then real-time location prediction accuracy is improved, but operational efficiency decreases due to maintenance challenges
Solution Approach 1:
The virtual map system is self-updating and self-verifying. It automatically predicts object locations based on conveyor zone data and verified photo-eye readings at discharge points, without requiring manual intervention or maintenance. The system self-corrects any discrepancies in location prediction by adjusting the virtual map based on actual discharge point observations.
3Reliability
If photo-eye feedback is heavily依赖 for zone control, then control reliability is improved, but system adaptability decreases due to infrastructure rigidity
Solution Approach 1:
The patent introduces dynamic elements to the system by using predicted object movements and time-based zone activation. The virtual map is continuously updated based on predicted object positions and conveyor speeds, allowing the system to adapt to varying operational conditions without relying on rigid photo-eye feedback infrastructure. Zones are activated dynamically based on predicted object arrival times rather than fixed sensor triggers.
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
Enables efficient and reliable operation of conveyor zones with reduced reliance on photo-eyes, minimizing infrastructure complexity and maintenance, while maintaining accurate object location tracking and control.
Implementation Method 1
detecting a presence of the object in a first zone of the conveyor system using a first photo-eye
Data Source
AI summary
Various embodiments are directed to predicting and mapping a location of an object transported by a conveyor system with respect to a sequence of zones of the conveyor system. An example method includes detecting the presence of the object in a first zone using a first photo-eye. The first zone is controlled with a first roller rotational speed. The method further includes generating a virtual map to describe a location of the object. The location of the object is dynamically determined at least by predicting the object's presence in a given zone using a roller rotational speed of one or more preceding zones and configuring a roller rotational speed of the given zone in accordance with the object predicted to be present in the given zone. The method further includes evaluating the virtual map's accuracy with detecting the object in a final zone using a second photo-eye.