Pollution source determination robot cleaner and operating method thereof
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Solution Overview
Problem
Robot cleaners struggle to efficiently identify and adapt to changing pollution sources in real-time due to the lack of current pollution degree reflection in their moving paths, leading to inefficient cleaning.
Innovation Solution
A robot cleaner equipped with artificial intelligence models and sensors to identify pollution sources based on event occurrences, update mapping information, and optimize moving paths accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot cleaner sets a moving path based on a pollution map, then the moving path is optimized, but the current degree of pollution of each location is not reflected, leading to inefficient cleaning
Solution Approach 1:
The robot cleaner continuously monitors pollution levels in real-time and feeds this information back to update the pollution map dynamically. This allows the cleaning path to be adjusted based on current pollution conditions rather than relying on static historical data, thereby resolving the contradiction between path optimization and current pollution information reflection.
Solution Approach 2:
The pollution map is transformed from a static structure to a dynamic one that continuously updates with real-time pollution data. This dynamic update mechanism ensures that the moving path optimization is based on current pollution conditions, enabling the robot to adapt its cleaning strategy to changing environmental conditions.
2Measurement precision
If the robot cleaner directly moves to each location to identify pollution degree, then current pollution information is obtained, but the moving path becomes inefficient and time-consuming
Solution Approach 1:
The robot cleaner performs preliminary pollution mapping by moving to locations and identifying pollution sources in advance. This preliminary action creates a pollution map that can be used for future cleaning operations, eliminating the need to revisit every location and thereby reducing time loss while maintaining pollution identification accuracy.
Solution Approach 2:
Instead of directly measuring pollution at every location in real-time, the robot creates a copy of the pollution distribution through preliminary mapping. This pollution map serves as a representative model that can be used for path optimization without requiring continuous direct measurement, thus reducing time consumption while preserving measurement precision.
3Productivity
If the robot cleaner uses AI models to identify pollution sources and update mapping information, then cleaning path optimization is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical sensing and mapping systems with AI-based models that process sensor data. Instead of using sophisticated hardware to directly identify pollution sources, the system uses software-based AI models to analyze sensor readings and infer pollution sources, thereby reducing mechanical complexity while maintaining or improving cleaning path optimization.
Data Source
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Figure 1b
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AI summary
A robot cleaner is provided. The robot cleaner according to the disclosure includes a driver including a drive motor configured to cause the robot cleaner to move, a memory storing information on a pollution map for the degree of pollution for each location in a map corresponding to a place in which the robot cleaner is located and information on locations of a plurality of objects on the map, and a processor, wherein the processor is configured to: identify a pollution source among the plurality of objects based on information on the locations of the plurality of objects and the pollution map, and control the driver to move the robot cleaner based on the location of the identified pollution source on the map.