Vision-Based Loading Machine Alignment Using Fiducial Markers
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Solution Overview
Problem
Aligning loading machines with material receptacles is a complex task that requires significant experience and can lead to equipment damage due to large sizes and forces involved, with existing automation efforts not fully addressing the precision needed.
Innovation Solution
A perception-based alignment system that uses a visual sensor network and electronic controller to associate fiducial markers with alignment data records, allowing for precise alignment and positioning of loading machines relative to material receptacles through manual or autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment and positioning is performed by an operator, then flexibility and adaptability are maintained, but alignment precision and consistency deteriorate due to the complex and experience-dependent nature of the task
Solution Approach 1:
The patent replaces manual mechanical alignment operations with an automated vision-based system. Cameras capture images of fiducial markers on the material receptacle, and a computer automatically calculates positioning parameters and controls the loading machine's movements, substituting human operator skills with automated optical-mechanical systems.
Solution Approach 2:
The system creates a visual copy of the physical workspace through fiducial markers and camera imaging. The markers serve as digital proxies for the material receptacle's position and orientation, allowing the computer to calculate precise alignment parameters without direct physical measurement.
2Ease of operation
If automation is increased to reduce operator experience requirements, then operational ease improves, but system complexity increases
Solution Approach 1:
The vision-based alignment system serves multiple functions: it detects the material receptacle's position, determines its orientation, calculates optimal loading parameters, and guides the loading machine's movements. This multi-functional approach consolidates several alignment tasks into a single integrated system.
Solution Approach 2:
The patent introduces fiducial markers as intermediary objects between the loading machine and the material receptacle. These markers facilitate communication between the vision system and the physical workspace, enabling automatic detection and alignment without requiring direct complex sensing of the receptacle itself.
3Reliability
If precise alignment is achieved through manual operation, then equipment damage risk is reduced, but time consumption increases due to the complex alignment procedure
Solution Approach 1:
The system performs preliminary alignment calculations and position determinations before the actual loading operation begins. By pre-calculating the optimal positioning parameters based on fiducial marker detection, the system eliminates time-consuming trial-and-error adjustments during the loading process itself.
Solution Approach 2:
The vision system continuously monitors the loading machine's position relative to the material receptacle and provides real-time feedback for adjustments. This closed-loop control ensures precise alignment while minimizing the time required for corrections, as the system can quickly detect and compensate for positioning errors.
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
Enhances the accuracy and efficiency of loading operations by allowing for precise alignment and positioning, reducing the risk of equipment damage and improving operator training through automated assistance.
Implementation Method 1
A visual sensor network can be configured to image a fiducial marker associated with the material receptacle
Data Source
AI summary
A perception-based alignment system can assist in aligning a loading machine with a material receptacle. The perception-based alignment system can be associated with a visual sensor network that can capture an image of the material receptacle. A fiducial marker is associated with the material receptacle. In an aspect, the perception-based alignment system can record an alignment data record of a loading operation and associate the alignment data record with the fiducial marker and, in another aspect, the perception-based alignment system can retrieve and execute the alignment data record to assist with a subsequent loading operation.