Method and apparatus for executing cleaning operation
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Solution Overview
Problem
Robotic cleaning systems lack an efficient method to determine and prioritize cleaning targets within a space based on contamination levels, leading to suboptimal cleaning operations across various environments and modes.
Innovation Solution
A robotic cleaning system that generates contamination map data based on real-time contamination levels, using machine learning models to identify and prioritize cleaning targets, allowing for adaptive operation modes and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic cleaning systems use general rule-based smart systems, then they can operate with simple control logic, but they cannot autonomously learn and adapt to different contamination levels and environments
Solution Approach 1:
The robotic cleaning system uses machine learning models that autonomously learn from contamination data without requiring external programming or manual intervention. The system self-improves by processing contamination map data and automatically adjusting cleaning strategies, enabling adaptive behavior while maintaining relatively simple system architecture.
2Productivity
If robotic cleaning systems clean entire spaces uniformly, then they ensure comprehensive coverage, but they waste time and energy on already clean areas
Solution Approach 1:
The system generates contamination maps that identify specific contaminated areas within the cleaning space, then directs cleaning operations only to those localized regions. This approach applies different cleaning intensity and priority to different areas based on their actual contamination levels, eliminating waste of time and energy on already clean areas while maintaining comprehensive coverage of contaminated zones.
Solution Approach 2:
The system performs preliminary scanning and contamination detection before executing cleaning operations. By generating contamination maps in advance and identifying target areas, the system can plan and execute efficient cleaning paths that avoid already clean areas, significantly improving productivity while reducing time loss.
3Measurement precision
If robotic cleaning systems operate without contamination map data, then they can simplify data processing, but they cannot determine prioritized cleaning targets based on contamination levels
Solution Approach 1:
The system introduces contamination map data as an intermediary representation that bridges raw sensor data and cleaning decision-making. The contamination maps organize and structure contamination information in a processed, easily interpretable format, enabling precise contamination level detection and priority determination without requiring complex real-time data processing during cleaning operations.
Data Source
AI summary
A robotic cleaning apparatus for performing a cleaning operation and a method of cleaning a cleaning space therefor are provided. The method includes acquiring contamination data indicating a contamination level of the cleaning space, acquiring contamination map data based on the contamination data, determining at least one cleaning target area in the cleaning space, based on a current time and the contamination map data, and cleaning the determined at least one cleaning target area. The method and apparatus may relate to artificial intelligence (AI) systems for mimicking functions of human brains, e.g., cognition and decision, by using a machine learning algorithm such as deep learning, and applications thereof.