Machine Vision System for Close-Range Target Positioning
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Solution Overview
Problem
Current machine vision systems face challenges in accurately determining the location of target elements, particularly at close distances, due to computational complexity, inaccuracies in traditional binocular camera visions, and errors from light ray angles and mechanical mounting deviations, which hinder efficient cargo handling in manufacturing and logistics.
Innovation Solution
A system that captures images of target elements, generates two-dimensional locations, and translates these into three-dimensional coordinates using a calibration table and traversing scheme, employing advanced electronics and simple components to achieve precise positioning and response time improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional binocular camera vision systems are used to determine target element locations, then the system can provide depth perception and positioning capability, but the system suffers from computational complexity and accuracy limitations due to light ray angle calculations and mechanical mounting deviations
Solution Approach 1:
The patent extracts and eliminates the problematic computational elements (light ray angle calculations and mechanical mounting deviation corrections) from the traditional binocular vision system. By using a calibrated camera system with pre-determined intrinsic parameters and a simplified geometric model, the system achieves accurate positioning without the computational burden of traditional methods.
Solution Approach 2:
The patent replaces the complex mechanical calibration and adjustment systems with a software-based calibration approach. The camera intrinsic parameters are determined through a calibration process using a known target pattern, eliminating the need for precise mechanical mounting and complex computational corrections during operation.
2Measurement precision
If complex image processing and artificial intelligence systems are employed to recreate human vision functions, then the system can achieve better object position resolution, but the system cost and computational requirements increase significantly
Solution Approach 1:
The patent uses simple, inexpensive camera components and basic image processing algorithms instead of expensive artificial intelligence systems. The solution relies on straightforward geometric calculations and pre-calibrated parameters, achieving accurate positioning with minimal computational resources and simple hardware.
Solution Approach 2:
The patent creates a simplified mathematical model that copies the essential function of human vision (depth perception and positioning) without attempting to replicate the biological complexity. The calibration process creates a mapping between image coordinates and real-world coordinates that achieves human-level positioning accuracy through simple computational geometry.
3Productivity
If traditional camera intrinsic analysis methods are used for target positioning, then the system can provide positioning capability, but remaining nonlinearities create errors beyond tolerance limits
Solution Approach 1:
The patent performs calibration in advance to determine the camera's intrinsic parameters and create a lookup table or calibration model. This preliminary action accounts for all nonlinearities and distortions before actual positioning operations, allowing fast, accurate positioning during cargo handling without real-time computational complexity.
Solution Approach 2:
The patent transforms the positioning problem from one requiring complex real-time nonlinear calculations to one using pre-determined parameters and simplified geometric relationships. By changing the approach from dynamic calculation to static calibration with lookup tables or pre-computed models, the system achieves both speed and accuracy.
Data Source
AI summary
This document describes machine vision systems and methods for determining locations of target elements. The described machine vision system captures and uses information gleaned from the captured target elements to determine the locations of these captured target elements.