Electronic apparatus and controlling method thereof
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Solution Overview
Problem
Robot cleaners lack optimized driving algorithms tailored to their actual operating environments, leading to suboptimal performance due to initial parameter settings not considering the specific place they operate in.
Innovation Solution
An electronic apparatus that updates its driving algorithm using an artificial intelligence model, trained with sensing data from the environment, to optimize parameter values for improved performance, including a communicator, memory for storing AI models, and a processor to simulate driving and transmit updated parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot cleaner uses initial parameter settings for the driving algorithm, then the device complexity is low, but the driving performance is not optimized for the actual environment
Solution Approach 1:
The system performs preliminary simulation of the driving algorithm in a virtual environment before actual deployment. By pre-training the AI model with sensing data and simulating various driving scenarios, the system optimizes parameter values in advance, allowing the robot to achieve better driving performance without increasing the complexity of the actual hardware or algorithm implementation.
2Adaptability or versatility
If the robot cleaner operates without environment-specific optimization, then the ease of operation is high, but the adaptability to different environments is poor
Solution Approach 1:
The robot cleaner performs self-learning by collecting sensing data from its actual operating environment and using this data to train the AI model. The system automatically optimizes its own driving algorithm parameters through simulation and feedback, enabling it to adapt to different environments without requiring manual reconfiguration or complex user intervention.
3Measurement precision
If the robot cleaner collects and processes sensing data for AI training, then the measurement precision of the environment is improved, but the loss of time for data processing increases
Solution Approach 1:
The system creates a virtual copy of the real environment by generating a map from sensing data collected by the robot cleaner. This digital replica allows the AI model to be trained and optimized in the virtual environment without requiring the robot to physically re-explore the space, significantly reducing the time needed for data processing while maintaining high measurement precision.
Data Source
AI summary
An electronic apparatus is provided. The electronic apparatus includes a communicator comprising communication circuitry, a memory storing information on an artificial intelligence model, and a processor configured to: obtain a map generated based on sensing data obtained by an external electronic apparatus, simulate driving of the external electronic apparatus on the obtained map based on a plurality of parameter values and obtain driving result data for the plurality of parameter values, train the artificial intelligence model based on the plurality of parameter values and the obtained driving result data and obtain a plurality of parameter values related to driving of the external electronic apparatus, and control the communicator to transmit the plurality of obtained parameter values to the external electronic apparatus.