Robot cleaner for performing cleaning using artificial intelligence and method of operating the same
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Solution Overview
Problem
Conventional robot cleaners lack the ability to adapt suction output and driving speed to various cleaning environments, leading to inefficient cleaning performance.
Innovation Solution
A robot cleaner utilizing a reinforcement learning model with a deep learning algorithm to determine optimal suction output and driving speed based on acquired cleaning environment information, employing an artificial neural network compensation model for adaptive operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional robot cleaners use fixed cleaning modes (normal mode and turbo mode), then the device complexity is reduced and ease of operation is improved, but the adaptability to various cleaning environments deteriorates
Solution Approach 1:
The robot cleaner uses sensors to detect cleaning environment information (such as dust levels, floor type, obstacles) and feeds this information back to the processor. The processor then adjusts suction output and driving speed in real-time based on the detected conditions, enabling adaptive cleaning without requiring complex manual intervention from the user.
Solution Approach 2:
The robot cleaner autonomously determines optimal cleaning parameters by processing environment information itself, without requiring user input or manual mode selection. The system self-adjusts suction power and movement speed based on real-time sensor data, making the cleaning process self-adaptive and eliminating the need for simple fixed modes.
2Productivity
If the robot cleaner uses a reinforcement learning model with deep learning algorithm, then the cleaning performance is improved and adaptability is enhanced, but the device complexity and computational requirements increase
Solution Approach 1:
The reinforcement learning model is trained in advance with large amounts of cleaning data to learn optimal cleaning strategies for various environments. This preliminary training phase allows the model to develop intelligent decision-making capabilities before deployment, enabling the robot to adapt to new cleaning scenarios without requiring complex real-time computations during actual cleaning operations.
Solution Approach 2:
The patent replaces traditional mechanical control systems (fixed modes with simple switches) with an intelligent software-based reinforcement learning model. This substitution enables the robot to automatically optimize cleaning parameters through learned patterns rather than pre-programmed rules, significantly improving adaptability while the computational complexity is managed through efficient model architecture.
3Productivity
If the robot cleaner optimizes suction output and driving speed based on cleaning environment information, then the cleaning efficiency is improved, but the power consumption may increase
Solution Approach 1:
The robot cleaner dynamically adjusts suction output and driving speed based on real-time cleaning environment information rather than operating at fixed levels. The system increases power consumption only when and where needed (e.g., higher suction in dusty areas, adjusted speed on different floor types), and reduces power consumption in cleaner or more favorable conditions, optimizing the balance between cleaning efficiency and energy usage.
Data Source
AI summary
A robot cleaner for performing cleaning using artificial intelligence includes a suction unit configured to suction dust, a driving unit to drive the robot cleaner, a memory configured to store a compensation model for inferring optimal suction output and driving output for cleaning environment information for learning, and a processor configured to acquire cleaning environment information, determine a suction output value and a driving speed of the robot cleaner from the acquired cleaning environment information using the compensation model, control the suction unit to suction the dust with the determined suction output value, and control the driving unit to drive the robot cleaner at the determined driving speed.