Mobile robot and control method thereof
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Solution Overview
Problem
Existing mobile robots lack the ability to autonomously learn and respond to user behavior patterns, particularly in cleaning areas where foreign materials are present, leading to inefficient cleaning and lack of personalized service.
Innovation Solution
A mobile robot equipped with detection sensors for movement and foreign materials, which learns behavior patterns by temporarily storing and cleaning areas where foreign materials are detected, and adapts its cleaning protocol based on real-time observations and sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the mobile robot cleans the entire area systematically, then the overall cleaning coverage is improved, but the response time to foreign materials caused by user behavior patterns deteriorates
Solution Approach 1:
The mobile robot performs preliminary learning of user behavior patterns during idle periods or while cleaning other areas. By temporarily storing detected movements and later confirming whether they cause foreign materials, the robot prepares in advance for likely cleaning needs, enabling rapid response when behavior patterns are recognized without sacrificing systematic cleaning coverage
Solution Approach 2:
The robot uses feedback from foreign material detection to validate and refine its behavior pattern library. When a foreign material is detected, the system confirms the corresponding movement as a valid behavior pattern, creating a closed-loop learning mechanism that improves response accuracy over time while maintaining efficient systematic cleaning
2Productivity
If the mobile robot learns and responds to behavior patterns in real-time, then the cleaning efficiency is improved, but the device complexity increases
Solution Approach 1:
The learning system is segmented into distinct functional modules: movement detection unit, temporary storage unit, foreign material detection unit, and behavior pattern determination unit. This modular segmentation allows the complex learning process to be broken down into manageable, independent components that can be developed and maintained separately, reducing overall system complexity while maintaining high cleaning efficiency
Solution Approach 2:
A behavior pattern library serves as an intermediary data structure between raw sensor inputs and cleaning control decisions. This intermediary layer abstracts the complex learning process into simple pattern-matching operations, reducing the computational complexity required for real-time decision-making while maintaining high cleaning efficiency through intelligent pattern recognition
3Manufacturing precision
If the mobile robot cleans areas based on learned behavior patterns, then the cleaning precision is improved, but the loss of time for learning and validation increases
Solution Approach 1:
The system employs periodic action by conducting foreign material detection at specific intervals or trigger points rather than continuously. The robot temporarily stores movement data and validates it against behavior patterns at predetermined moments, reducing the time required for learning validation while maintaining high cleaning precision through targeted verification
Solution Approach 2:
The robot applies partial validation by confirming only the specific behavior patterns that are most likely to cause foreign materials, rather than validating all possible movements. This selective validation approach reduces the time required for learning while maintaining high cleaning precision by focusing on the most critical patterns
Data Source
AI summary
The present disclosure discloses a mobile robot and a control method thereof. More particularly, when a movement is detected in an area to be cleaned, a mobile robot learns whether the detected movement is a behavior pattern of causing a foreign material or not. The mobile robot performs a suitable cleaning when the movement of causing the foreign material is found or detected later.