Pollution source determination robot cleaner and operating method thereof
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Solution Overview
Problem
Existing robot cleaners face challenges in identifying the current degree of pollution at each location without physically moving to each spot, leading to inefficient cleaning paths based on outdated pollution maps.
Innovation Solution
A robot cleaner equipped with a processor, memory, and AI models to identify pollution sources by analyzing pollution maps and sensor data, determining the main pollution source for each event, and adjusting its moving path dynamically based on real-time data from cameras and sensors.
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 optimization is improved, but the current degree of pollution at each location is not reflected leading to inefficient cleaning
Solution Approach 1:
The robot cleaner performs preliminary actions by proactively moving to locations with high pollution degrees identified from event data before actual cleaning is needed. The processor predicts pollution sources based on event information (cooking, smoking, etc.) and directs the robot to those locations in advance, rather than waiting to detect pollution after it occurs. This preliminary positioning ensures the robot is ready to clean immediately when pollution is detected, improving cleaning efficiency while maintaining accurate pollution information.
2Measurement precision
If the robot cleaner directly moves to each location to identify pollution degree, then the current pollution information is obtained, but the cleaning process becomes time-consuming and inefficient
Solution Approach 1:
The robot cleaner uses event information as an intermediary to indirectly identify pollution sources without physically visiting every location. The processor analyzes event data (such as cooking events, smoking events, pet events) to infer where pollution is likely to occur, then moves only to those specific high-probability locations. This intermediary approach using event-based inference maintains accurate pollution identification while dramatically reducing the time and distance the robot must travel compared to checking every location directly.
3Device complexity
If the robot cleaner uses a general pollution map for moving path optimization, then the path planning is simplified, but the specific pollution sources mapped to events are not identified
Solution Approach 1:
The pollution map is segmented into multiple layers: a basic spatial map for general navigation and an event-based pollution layer for specific pollution source identification. The processor separately maintains simple path planning based on the basic map while overlaying event-derived pollution information to identify specific pollution sources. This segmentation allows the system to keep path planning relatively simple while simultaneously achieving accurate pollution source identification through the additional event-based layer.
Data Source
Figure 1a
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.