Cleaner and method for controlling same
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Solution Overview
Problem
Existing robot cleaners face issues with positional accuracy and obstacle avoidance when map information is inaccurate, leading to potential loss of position and erroneous detection during autonomous travel.
Innovation Solution
A robot cleaner equipped with a sensing unit, including a camera sensor, generates map information by determining driving cost values for each grid based on feature detection and environmental factors like brightness and feature distribution, allowing it to set a travel path that avoids dangerous areas and ensures accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the cleaner travels directly to the destination using the shortest path, then the travel time is reduced, but the cleaner may enter areas with insufficient information which causes positional loss or erroneous detection
Solution Approach 1:
The system performs preliminary evaluation of information sufficiency for each grid cell before the cleaner travels to the destination. By pre-assessing whether each grid has sufficient environmental information (brightness, feature distribution) for reliable position detection, the system can plan a travel path that avoids information-deficient areas, thus preventing positional loss before it occurs.
Solution Approach 2:
The system uses feedback from environmental sensors (brightness sensors, feature extraction from camera images) to continuously evaluate the information quality of each grid cell. This feedback mechanism allows the cleaner to adjust its travel path in real-time, avoiding areas where position detection reliability is insufficient while still progressing toward the destination efficiently.
2Measurement precision
If the cleaner avoids areas with insufficient information, then the position detection accuracy is improved, but the travel path becomes longer and more complex
Solution Approach 1:
The cleaning area is divided into discrete grid cells, and each grid is independently evaluated for information sufficiency based on brightness and feature distribution. This segmentation allows the system to create a simplified map of reliable vs. unreliable areas, making path planning more manageable despite the increased accuracy requirements.
Solution Approach 2:
Different grid cells are assigned different quality ratings based on their information sufficiency (brightness levels, feature distribution). The path planning algorithm then selects paths that pass through high-quality grids, ensuring position detection accuracy is maintained in each local area while achieving global optimization of the travel route.
3Speed
If the cleaner uses existing map information for navigation, then the navigation speed is maintained, but the map information may be inaccurate leading to erroneous position detection
Solution Approach 1:
Before the cleaner begins navigation using map information, the system performs preliminary assessment of each grid cell's information quality. This preliminary action ensures that the map information being used is verified to be accurate and sufficient for reliable position detection, preventing errors while maintaining navigation speed.
Solution Approach 2:
The system continuously compares real-time sensor data (brightness measurements, feature extraction) with the stored map information. This feedback mechanism verifies the accuracy of map information during navigation, allowing the cleaner to maintain high navigation speed while ensuring position detection accuracy through ongoing validation.
Data Source
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AI summary
To successfully deal with a problem to be solved by the present invention, a cleaner for performing automatic driving according to an embodiment of the present invention comprises: a body; a driving unit for moving the body; a detecting unit for acquiring information relating to an environment around the body while the body runs in a cleaning area; and a control unit which generates map information corresponding to the cleaning area on the basis of the information acquired by the detecting unit, configures a driving cost value for each of multiple grids included in the map information, and configures a driving route of the body on the basis of the configured driving cost value.