Image-Based Working Area Recognition for Wire-Free Robot Mowing
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Solution Overview
Problem
Existing methods for defining working areas for mobile robots, such as robot mowers, rely on buried boundary wires, which are labor-intensive and limit the shape of the lawn, requiring complex wire configurations and increased costs.
Innovation Solution
A method and system that uses image recognition to determine the working area by processing HSV images, separating H and V channels, performing binarization, and collecting statistics on histogram peaks and area sizes to accurately identify the robot's position and distinguish between working and non-working areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If boundary wires are buried to calibrate the lawn working area boundary, then the working area boundary can be defined, but it requires a lot of manpower and material resources and increases usage costs
Solution Approach 1:
The patent extracts the boundary definition function from the physical wire infrastructure and relocates it to the image processing system. By separating the boundary calibration function from the wire medium, the system can define working areas using only image data, eliminating the need for extensive wire burial while maintaining boundary definition capability
Solution Approach 2:
The patent creates a digital copy of the boundary information through image processing. Instead of using physical wires to mark boundaries, the system captures visual information of the lawn and boundary markers, processes this image data to extract boundary coordinates, and creates a digital representation of the working area that can be used for robot navigation
2Measurement precision
If boundary wires are buried to calibrate the lawn working area boundary, then the working area boundary can be defined, but it limits the shape of the lawn working area
Solution Approach 1:
The patent transforms the static, rigid wire-based boundary system into a dynamic, flexible image-processing-based system. The working area boundary is no longer constrained by fixed wire layouts but can be dynamically determined from image data, allowing the system to adapt to various lawn shapes including irregular and curved boundaries that would be difficult to implement with traditional wire methods
Solution Approach 2:
The patent changes the fundamental parameter of boundary representation from physical wire coordinates to image-based pixel coordinates. This parameter change enables the system to represent boundaries with continuous curves and irregular shapes, overcoming the geometric limitations of wire-based systems that require corners to be at least 90 degrees
3Measurement precision
If boundary wires are buried to calibrate the lawn working area boundary, then the working area boundary can be defined, but it requires complex wire configurations
Solution Approach 1:
The patent replaces the mechanical wire burial and configuration system with an optical image processing system. Instead of physically installing and configuring wires to match the lawn boundary, the system uses cameras to capture images and algorithms to automatically extract boundary information, eliminating the complex mechanical configuration process while maintaining or improving boundary definition accuracy
Data Source
AI summary
A method for recognizing a working area based on an image includes the steps of: obtaining an original image; separating an H channel image from the original image; performing binarization processing on the original image to form a first binary image, wherein the first binary image comprises a working area and a non-working area that have different pixel values; collecting statistics on a histogram of the H channel image, and obtaining a first parameter representing a peak value of the histogram; collecting statistics on a second parameter representing a size of the non-working area in the first binary image based on the first binary image; and recognizing a working area according to magnitude relations of the first parameter and the second parameter with preset parameter thresholds. Other related methods, systems, and robots are disclosed.