AI Cleaner Zone-Based Route Planning to Reduce Repeated Coverage
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Solution Overview
Problem
Robot cleaners inefficiently clean spaces due to inadequate route planning, often getting stuck or repeating areas, as they consider only a limited radius and not the entire cleaning area, leading to uneven cleaning times and potential movement restrictions.
Innovation Solution
An artificial intelligence cleaner that divides the cleaning space into areas based on cleaning logs, sets a cleaning route, and adjusts cleaning modes and priorities for each area type, using machine learning algorithms to optimize cleaning efficiency and reduce the likelihood of failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the robot cleaner performs cleaning while avoiding obstacles in a predetermined radius around the cleaner, then the cleaner can navigate locally, but it wanders in previously visited areas and repeats cleaning the same areas
Solution Approach 1:
The patent divides the cleaning space into multiple zones (first cleaning zone, second cleaning zone, third cleaning zone) based on obstacle density and cleaning status. The cleaner selectively operates in different zones according to predetermined criteria, preventing it from repeatedly cleaning the same areas while maintaining local navigation capabilities.
Solution Approach 2:
The patent implements dynamic zone classification where cleaning zones are updated in real-time based on the cleaner's position, obstacle detection, and cleaning progress. The cleaner dynamically transitions between zones (e.g., from first to second cleaning zone when obstacles are detected), enabling adaptive route planning that improves cleaning efficiency while maintaining local avoidance capabilities.
2Area of stationary object
If the robot cleaner operates in areas with many obstacles, then it can cover more ground, but cleaning takes a long time and movement is restricted
Solution Approach 1:
The patent applies different cleaning strategies to different zones based on their characteristics. In the first cleaning zone (low obstacle density), the cleaner performs thorough cleaning. In the second cleaning zone (high obstacle density), the cleaner adjusts its path to minimize time loss while still covering the area. This localized adaptation allows the cleaner to cover more ground without excessive time penalty.
Solution Approach 2:
The patent pre-classifies the cleaning space into zones with different obstacle densities before cleaning begins. This preliminary classification allows the cleaner to plan its route in advance, knowing which areas will require more time and which can be covered quickly, thereby optimizing the overall cleaning time while maintaining extensive coverage.
3Device complexity
If the robot cleaner uses a fixed cleaning route, then the cleaning process is simple to control, but certain areas are cleaned more than others and efficiency is reduced
Solution Approach 1:
The patent implements a dynamic zone-based cleaning system where the cleaner transitions between different cleaning zones (first, second, third zones) based on real-time conditions such as obstacle detection and cleaning progress. This dynamic approach allows the cleaning route to adapt to the environment, ensuring uniform cleaning coverage across all areas while maintaining relatively simple control logic through predetermined zone transition rules.
Data Source
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AI summary
Disclosed herein is an artificial intelligence cleaner. The artificial intelligence cleaner includes a memory configured to store a simultaneous localization and mapping (SLAM) map for a cleaning space; a driving unit configured to drive the artificial intelligence cleaner; and a processor configured to collect a plurality of cleaning logs for the cleaning space, divide the cleaning space into a plurality of cleaning areas using the SLAM map and the collected plurality of cleaning logs, determine a cleaning route of the artificial intelligence cleaner in consideration of the divided cleaning areas, and control the driving unit according to the determined cleaning route.