Robot Cleaner Camera-Based Dust Detection for Adaptive Route Planning
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Solution Overview
Problem
Conventional robot cleaners waste time and energy by cleaning areas that do not require cleaning due to their reliance on predetermined driving routes, leading to inefficient cleaning operations.
Innovation Solution
The robot cleaner includes a camera to identify dust levels, a vacuum for cleaning, a driver for movement, and a processor to control the system, allowing it to detect dust areas and adjust cleaning routes dynamically based on dust levels, optimizing cleaning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot cleaner follows a predetermined driving route and cleans all areas, then the cleaning operation covers the entire driving route, but the robot cleaner spends excessive time and consumes unnecessary power in areas where dust does not exist
Solution Approach 1:
The system performs preliminary dust detection by capturing images of the cleaning area before the actual cleaning operation. The processor analyzes these images to identify dust locations and generates a dust map, allowing the robot to plan its cleaning route in advance and avoid areas without dust, thereby reducing unnecessary cleaning time.
Solution Approach 2:
The system continuously captures images during movement, processes them to detect dust in real-time, and uses this feedback information to dynamically adjust the cleaning route. The processor compares detected dust locations with the predetermined route and modifies the path to prioritize dust-containing areas, improving cleaning efficiency while reducing time spent in dust-free zones.
2Productivity
If the robot cleaner follows a predetermined driving route and cleans all areas, then the cleaning operation is systematic, but the robot cleaner consumes unnecessary power in areas where dust does not exist
Solution Approach 1:
The system performs preliminary dust detection by capturing images of the cleaning area before the actual cleaning operation. The processor analyzes these images to identify dust locations and generates a dust map, allowing the robot to plan its cleaning route in advance and avoid areas without dust, thereby reducing unnecessary power consumption.
Solution Approach 2:
The system continuously captures images during movement, processes them to detect dust in real-time, and uses this feedback information to dynamically adjust the cleaning route. The processor compares detected dust locations with the predetermined route and modifies the path to prioritize dust-containing areas, reducing power consumption by avoiding dust-free zones.
3Ease of operation
If the robot cleaner relies on predetermined driving route, then the driving path is simple to plan, but the cleaning operation cannot adapt to areas where cleaning is not necessary
Solution Approach 1:
The system maintains a predetermined driving route for simple initial planning but continuously captures images during movement and processes them to detect dust in real-time. This feedback loop allows the processor to dynamically adjust the route, deviating from the predetermined path when dust is detected in unplanned areas or skipping areas that are already clean, thereby achieving both simplicity and adaptability.
Solution Approach 2:
The system transitions from a static predetermined route to a dynamic adaptive route. The processor continuously updates the cleaning path based on real-time dust detection results, allowing the robot to adapt its movement pattern to the actual dust distribution while maintaining the overall structure of the predetermined route for simplicity.
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
The system enables targeted cleaning of only dusted areas, reducing cleaning time and energy consumption by avoiding unnecessary cleaning, thus enhancing efficiency.
Implementation Method 1
outputs a light toward a bottom surface located in front of the robot cleaner through a light emitter and identifies an amount of dust existing on the bottom surface
Data Source
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AI summary
A robot cleaner and a control method thereof are provided. The robot cleaner includes a camera, a vacuum, a driver, and at least one processor configured to acquire at least one image through the camera while the robot cleaner is driving along a first driving route, identify an amount of dust in areas within a view angle of the camera based on the acquired image, control the suction part to perform a cleaning operation for an area to be cleaned identified based on the identified amount of dust, and acquire first map data indicating an amount of dust of an area wherein the robot cleaner drove among the areas within the view angle of the camera and second map data indicating an amount of dust of an area wherein the robot cleaner is going to drive among the areas within the view angle of the camera based on the identified amount of dust.