Method and apparatus for executing cleaning operation
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Solution Overview
Problem
Current robotic cleaning systems lack the ability to efficiently determine and prioritize cleaning target areas based on contamination levels in varying environments and operation modes, leading to suboptimal cleaning performance.
Innovation Solution
A robotic cleaning system and method that generates contamination map data over time, using a learning model to determine cleaning target areas and prioritize them based on current conditions, allowing for efficient operation in multiple modes and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a robotic cleaning system uses traditional rule-based control methods, then the system structure is simple, but the cleaning efficiency and adaptability to different contamination levels are insufficient
Solution Approach 1:
The patent applies parameter changes by transitioning from fixed rule-based control to dynamic AI-based control parameters. The system continuously adjusts cleaning parameters (such as suction power, movement speed, and cleaning priority) based on real-time contamination level detection and AI analysis, enabling adaptive optimization of cleaning efficiency without requiring complete system redesign
Solution Approach 2:
The patent replaces traditional mechanical rule-based control systems with AI-based intelligent decision-making systems. Instead of pre-programmed cleaning paths and fixed contamination response rules, the system uses machine learning models to autonomously determine cleaning strategies, target areas, and resource allocation based on real-time environmental perception
2Reliability
If the robotic cleaning system cleans the entire cleaning space uniformly, then the cleaning coverage is complete, but the time and energy consumption increase
Solution Approach 1:
The patent applies local quality by transitioning from uniform cleaning across the entire space to differentiated cleaning strategies for different regions. The AI system analyzes contamination maps to identify high-priority contaminated areas and allocates cleaning resources preferentially to these regions, while reducing or skipping cleaning in low-contamination areas, thereby maintaining effective cleaning coverage while significantly reducing time and energy consumption
Solution Approach 2:
The patent segments the cleaning space into multiple zones based on contamination levels detected by sensors and analyzed by the AI system. The cleaning space is divided into high-priority, medium-priority, and low-priority areas, allowing the robotic cleaner to process different segments with different cleaning intensities and time allocations, optimizing overall cleaning efficiency
3Adaptability or versatility
If the robotic cleaning system operates in multiple operation modes, then the adaptability to different environments improves, but the control complexity increases
Solution Approach 1:
The patent applies universality by designing a unified AI-based control framework that handles multiple operation modes (such as automatic mode, manual mode, energy-saving mode, and rapid cleaning mode) through a single intelligent decision-making system. The AI model automatically adjusts its behavior and parameters based on the selected mode and real-time conditions, eliminating the need for separate control logic for each mode and simplifying the overall control architecture
Data Source
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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.