Circular Marker Position Sensing for Low-Cost Mobile Robots
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Solution Overview
Problem
Existing self-position measurement technologies for mobile robots are inefficient due to high equipment costs and aesthetic or recognition issues with traditional markers, and require numerous sensors or complex machine learning processes.
Innovation Solution
A position measurement method using image acquisition and analysis to detect circular markers, calculating self-position based on aspect ratios and relative angles, allowing for autonomous travel control without the need for extensive sensor arrays or machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional markers (line markers, light sources, 2D barcodes) are used for position measurement, then position measurement can be achieved, but aesthetic appearance is spoiled and markers are susceptible to soiling and recognition failure
Solution Approach 1:
The patent uses circular markers with specific color patterns (e.g., colored circumferential bands on a white background) that are visually distinct and maintain recognition under various conditions. The color design allows the markers to be less obtrusive aesthetically while maintaining detectability through color-based image processing
Solution Approach 2:
The patent employs circular-shaped markers instead of linear or rectangular patterns. The circular geometry provides several advantages: it is more aesthetically pleasing, less obtrusive in environments, and maintains consistent recognition characteristics from different angles and distances, reducing recognition failure due to soiling or perspective distortion
2Measurement precision
If numerous sensors (ultrasonic radar, triangulation sensors, electromagnetic wave sensors) are used for position measurement, then position measurement capability is improved, but equipment cost increases and device complexity increases
Solution Approach 1:
The patent uses a single imaging device (camera) that serves multiple functions: detecting the circular markers for position measurement, determining the robot's orientation through aspect ratio analysis, and calculating distance through size analysis. This multi-functional approach eliminates the need for separate ultrasonic, electromagnetic, and other specialized sensors
Solution Approach 2:
The patent replaces complex mechanical sensor arrays with an optical imaging system combined with image processing algorithms. The position, orientation, and distance information is extracted through computational analysis of the captured images rather than through multiple physical sensing mechanisms
3Adaptability or versatility
If machine learning techniques are used for autonomous driving, then flexibility and adaptability are improved, but equipment cost for learning and arithmetic processing increases
Solution Approach 1:
The patent uses geometric parameters (aspect ratio, area size, circularity) that naturally vary with the robot's position and orientation relative to the markers. By tracking changes in these parameters over time, the system achieves adaptive navigation without requiring complex machine learning models or extensive arithmetic processing
Data Source
AI summary
A position measurement method includes a step of acquiring an image of the surroundings at a self-position, a step of detecting an area in which a circular shape appears in the image, and a step of measuring the self-position based on the aspect ratio of the area.