Cleaning Robot Dirt Recognition Using Image Block Analysis
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Solution Overview
Problem
Existing cleaning robots lack active dirt recognition capabilities, leading to inefficient cleaning processes and high energy consumption, as they rely on passive detection methods, requiring manual guidance and resulting in low accuracy and long cleaning times.
Innovation Solution
A dirt recognition device equipped with an image collecting module and an image processing module that divides the surface into blocks, extracts image information, and determines the dirtiest areas using gray-scale values and characteristic values, enabling active recognition and targeted cleaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the cleaning robot uses passive detection methods with dust sensors, then the robot can detect dirt when encountered, but the cleaning time is very long and cleaning efficiency is low
Solution Approach 1:
The robot performs preliminary scanning of the cleaning area to identify dirt locations before actual cleaning begins. The image collecting module captures images of the cleaning area, and the image processing module processes these images to locate dirt positions in advance, allowing the robot to plan an optimal cleaning path and avoid unnecessary movements.
Solution Approach 2:
The patent replaces the traditional passive dust sensor detection system with an active image-based detection system. Instead of relying on dust sensors that only detect dirt when the robot is directly over it, the image collecting module actively captures visual information about dirt locations, and the image processing module analyzes this information to determine cleaning priorities.
2Ease of operation
If the cleaning robot moves about in general cases without active recognition, then the robot can cover the cleaning area, but it takes very long time to clean and consumes excessive electric energy
Solution Approach 1:
The robot autonomously identifies dirt locations and plans its own cleaning path without human intervention. The image processing module automatically analyzes captured images, identifies dirt positions, and the control module generates an optimized cleaning path based on this information, allowing the robot to serve itself and eliminate unnecessary movements.
Solution Approach 2:
The system continuously monitors the cleaning area using the image collecting module, processes the visual information to identify dirt locations, and adjusts the cleaning path in real-time based on this feedback. This closed-loop control allows the robot to respond dynamically to the actual dirt distribution and optimize energy consumption.
3Measurement precision
If the robot divides the image into blocks and processes image information, then the dirt recognition accuracy is significantly improved, but the image processing complexity increases
Solution Approach 1:
The image processing module divides the captured cleaning area image into multiple blocks and processes each block independently to identify dirt positions. This segmentation approach allows the system to focus computational resources on specific areas of interest and improves dirt recognition accuracy by analyzing local characteristics of each block.
Data Source
AI summary
A cleaning robot a dirt recognition device thereof and a cleaning method of the robot are disclosed. The recognition device includes an image collecting module and an image processing module. The image collecting module may be used for collecting the image information of the surface to be treated by the cleaning robot and sending the image information to the image processing module. The image processing module may divide the collected image information of the surface to be treated into N blocks, extract the image information of each block and process the image information in order to determine the dirtiest surface to be treated that corresponds to one of the N blocks. Through the solution provided by the present invention, the cleaning robot can make an active recognition to the dirt such as dust, so that it can get into the working area accurately and rapidly.