Artificial intelligence cleaner and method of operating the same
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Solution Overview
Problem
Robot cleaners inefficiently clean spaces due to their inability to adapt cleaning routes and modes based on the specific characteristics of different areas, leading to prolonged cleaning times and uneven cleaning coverage.
Innovation Solution
An artificial intelligence cleaner that divides the cleaning space into multiple areas based on cleaning logs, classifies these areas, and sets appropriate cleaning routes and modes for each area type, prioritizing cleaning operations to optimize efficiency and reduce the likelihood of cleaning failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the robot cleaner performs cleaning while avoiding obstacles in a predetermined radius around the cleaner, then the cleaner can operate autonomously without user intervention, but the cleaner wanders in previously cleaned areas and cleaning efficiency decreases
Solution Approach 1:
The cleaning space is divided into multiple cleaning areas based on cleaning logs and area identification information. The cleaner segments the overall cleaning task into area-specific subtasks, allowing it to systematically progress through different zones rather than wandering randomly, thereby improving cleaning efficiency while maintaining autonomous operation
Solution Approach 2:
The cleaner performs preliminary analysis of cleaning logs to identify area characteristics and determine optimal cleaning routes before executing the cleaning task. By pre-planning the cleaning path based on historical data and area classification, the cleaner avoids wandering in previously cleaned areas and maintains high productivity
2Reliability
If the robot cleaner cleans areas with many obstacles by repeatedly navigating through them, then the cleaner attempts to clean difficult-to-reach areas, but cleaning time increases and cleaning may be restricted due to movement restrictions
Solution Approach 1:
The cleaner applies different cleaning strategies to different cleaning areas based on their specific characteristics. For areas with many obstacles, the system identifies these zones and adjusts the cleaning approach locally, such as modifying the route to minimize repeated navigation or adjusting cleaning parameters, thereby maintaining reliable coverage without excessive time loss
Solution Approach 2:
The cleaning route and mode are dynamically adjusted based on area identification information from cleaning logs. When the cleaner encounters areas with movement restrictions or many obstacles, the system adaptively changes the cleaning path and parameters in real-time, allowing the cleaner to maintain coverage reliability while reducing time spent on difficult areas
3Ease of operation
If the robot cleaner uses a fixed cleaning route for the entire cleaning space, then the cleaning process is simple to control, but certain areas are cleaned more than others and cleaning efficiency decreases
Solution Approach 1:
The cleaning space is segmented into multiple cleaning areas with different characteristics. Instead of applying a single fixed route to the entire space, the system creates area-specific cleaning segments, ensuring each zone is cleaned appropriately without over-cleaning or under-cleaning any particular area, thus improving efficiency while keeping control logic manageable
Solution Approach 2:
The cleaning parameters such as route, speed, and mode are changed based on area identification information. The system automatically adjusts these parameters for different cleaning areas according to their characteristics, optimizing cleaning efficiency for each zone while maintaining simple overall control through automated parameter adaptation
Data Source
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.