Leader-Controlled Imaging Device Program Switching Using Photogrammetry
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Solution Overview
Problem
Conventional machine vision systems require time-consuming and error-prone manual configuration changes when imaging task requirements change, leading to reduced production uptime and inefficient image capture and processing.
Innovation Solution
Implementing scout devices that capture images of target objects and transmit them to a leader device for analysis, allowing the leader device to automatically adjust machine vision jobs on follower devices based on dimension analysis and photogrammetric techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration changes are made to machine vision programs, then the system can be properly configured for different imaging tasks, but the process becomes time-intensive and reduces system uptime
Solution Approach 1:
The system performs self-configuration by automatically analyzing captured images, determining object dimensions using photogrammetry, and selecting appropriate machine vision programs without human intervention. This eliminates manual configuration time while maintaining accuracy through automated image analysis and program selection.
Solution Approach 2:
Multiple machine vision programs are pre-configured in the system for different object types and dimensions. When an object is captured, the system quickly selects and applies the appropriate pre-configured program, avoiding the need for time-consuming manual configuration while ensuring proper setup for each imaging task.
2Adaptability or versatility
If manual configuration changes are made to machine vision programs, then the system can adapt to different imaging requirements, but errors frequently occur resulting in wrong programs being executed
Solution Approach 1:
The system captures images of actual objects, analyzes their dimensions through photogrammetry, and uses this feedback to automatically select the correct pre-configured machine vision program. This closed-loop approach ensures the selected program matches the actual imaging requirements, eliminating manual errors while maintaining adaptability to different objects.
Solution Approach 2:
The system creates a digital representation of the object through image capture and photogrammetric analysis, then uses this copy to determine the appropriate program selection. This eliminates manual intervention errors while preserving the ability to adapt to various object types through accurate digital modeling.
3Adaptability or versatility
If manual configuration changes are made to machine vision programs, then the system can be adjusted for different production requirements, but the process adversely impacts system uptime
Solution Approach 1:
The system automatically performs configuration changes by analyzing captured images and selecting appropriate programs without requiring manual intervention. This maintains continuous operation and maximizes uptime while preserving production flexibility through automated adaptation to different imaging tasks.
Solution Approach 2:
Multiple programs are pre-configured for different production scenarios, enabling the system to quickly switch between them based on automatic image analysis. This eliminates downtime associated with manual reconfiguration while maintaining the flexibility to adapt to various production requirements.
4Productivity
If automated image analysis and program changing is implemented, then program change speed increases, but device complexity increases
Solution Approach 1:
The leader device performs multiple functions including image reception, photogrammetric analysis, program selection, and configuration management within a single integrated system. This consolidates complexity into one multi-functional device rather than distributing it across multiple specialized components, achieving fast program changes while managing system complexity.
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 fast, accurate, and efficient changes to machine vision jobs without human intervention, minimizing errors and maximizing system uptime and image capture efficiency.
Implementation Method 1
analyzing, by the leader device, the image of the target object using a photogrammetric technique to determine a dimension of the target object
Data Source
AI summary
Systems and methods for changing programs on imaging devices are disclosed herein. An example method includes capturing, by a first imaging device, an image of a target object, and transmitting the image of the target object to a leader device that is communicatively coupled to the first imaging device and a second imaging device. The second imaging device may be different from the first imaging device. The example method may further include analyzing, by the leader device, the image of the target object using a photogrammetric technique to determine a dimension of the target object, and changing a program executing on the second imaging device based on the dimension of the target object.