Artificial intelligence robot for performing cleaning using pollution log and method for same
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Solution Overview
Problem
Conventional robot cleaners lack the ability to adapt their cleaning operations based on the type, degree, and time of pollution, leading to inefficiencies and unsatisfactory cleaning experiences.
Innovation Solution
An artificial intelligence robot that classifies pollution logs into groups based on similarity, generates indoor area maps, determines suitable cleaning methods, and prioritizes cleaning tasks based on pollution type, degree, and available time, using a learning processor and map generator to optimize cleaning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot cleaner performs cleaning along a predetermined moving line, then the cleaning operation is simple and automated, but the cleaning efficiency and user satisfaction are insufficient because it cannot adapt to different pollution conditions
Solution Approach 1:
The system performs preliminary actions by collecting pollution information and storing it in pollution logs before actual cleaning operations. The learning processor analyzes this pre-collected data to classify pollution types, degrees, and locations, enabling the robot to adapt its cleaning strategy without adding complexity to the basic cleaning mechanism.
Solution Approach 2:
The patent replaces complex mechanical decision-making with information processing. Instead of using complex mechanical sensors and actuators to detect and respond to pollution, the system uses a learning processor to analyze pollution logs and determine appropriate cleaning methods, substituting mechanical complexity with computational intelligence.
2Productivity
If the robot cleaner considers pollution type, degree, and cleaning time, then cleaning efficiency and user satisfaction increase, but the cleaning operation becomes more complex
Solution Approach 1:
The system segments the cleaning task by classifying pollution into different groups based on type, degree, and location. The learning processor divides pollution logs into distinct categories, allowing the robot to apply different cleaning methods to different segments, thereby improving efficiency without requiring a single complex cleaning mechanism.
Solution Approach 2:
The system implements feedback by using cleaning evaluation result information to continuously improve its cleaning operations. The learning processor analyzes feedback from completed cleaning tasks and adjusts future cleaning methods, enabling the system to become more efficient over time without increasing operational complexity.
3Reliability
If the robot performs comprehensive cleaning considering all pollution factors, then cleaning quality improves, but the time required for cleaning increases
Solution Approach 1:
The system applies local quality by determining different cleaning methods for different pollution log groups based on their specific characteristics. Instead of using a uniform high-quality cleaning approach everywhere, the learning processor identifies which areas require intensive cleaning and which can be cleaned more quickly, thereby maintaining high cleaning quality where needed while reducing overall cleaning time.
Data Source
AI summary
The present invention provides an artificial intelligence includes a memory configured to store a plurality of pollution logs; a learning processor configured to classify the plurality of pollution logs into at least one pollution log group based on a similarity between pollution information; a map generator configured to generate an indoor area map to which location of each of at least one pollution log group is mapped; and a processor which determines a cleaning method for each of the at least one pollution log group and performs cleaning for each of the at least one pollution log group according to the determined cleaning method.