Cleaning Robot Floor Material Identification Using Vibration Signals
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Solution Overview
Problem
Existing floor material identification technologies are costly, unreliable, and prone to inaccurate identification due to the use of expensive ultrasonic sensors or complex visual hardware algorithms, and multi-sensor fusion schemes that can be confused by motor current changes when brushes are tangled with hair.
Innovation Solution
A method using vibration signals from a cleaning robot's accelerometer to identify floor materials by analyzing the ratio of low to high frequency band energy through frequency spectrum analysis, with training by a classifier to determine accurate identification parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasonic sensors are used for floor material identification, then identification accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive ultrasonic sensors with low-cost accelerometers that are already present in the cleaning robot for other functions. This principle of using cheap, readily available components instead of expensive specialized sensors directly resolves the contradiction between identification accuracy and cost.
Solution Approach 2:
The patent repurposes the accelerometer, which is originally designed for motion detection and navigation in cleaning robots, to also perform floor material identification. By making the accelerometer multi-functional, the system achieves new capabilities without additional hardware cost, resolving the contradiction between measurement precision and ease of manufacture.
2Measurement precision
If visual hardware with complex algorithms is used for floor material identification, then identification capability is improved, but implementation cost and efficiency deteriorate
Solution Approach 1:
The patent replaces complex visual processing systems with simple mechanical vibration analysis. Instead of using cameras and complex image recognition algorithms, the system uses accelerometers to capture vibration signals and applies straightforward frequency spectrum analysis, significantly reducing computational complexity while maintaining identification capability.
Solution Approach 2:
The patent extracts only the essential vibration characteristics from the complex signal environment by focusing on frequency spectrum analysis. Rather than processing entire visual scenes or complex multi-sensor data, the system isolates and analyzes specific vibration frequency bands, simplifying the identification process while maintaining accuracy.
3Adaptability or versatility
If multi-sensor fusion schemes are used for floor material identification, then identification coverage is improved, but reliability deteriorates due to motor current interference
Solution Approach 1:
The patent extracts and analyzes only the vibration signal component from the robot's operation, separating it from other interfering signals such as motor current variations. By focusing exclusively on acceleration data and its frequency spectrum, the system eliminates the reliability issues caused by multi-sensor fusion while maintaining identification coverage through careful signal processing.
Solution Approach 2:
The patent converts the naturally occurring vibrations during robot operation into useful identification signals. Rather than trying to eliminate all sources of vibration or interference, the system leverages the characteristic vibration patterns that occur when the robot moves across different floor materials, transforming what could be noise into a reliable identification feature.
4Adaptability or versatility
If rolling brush or edge brush is used for multi-sensor fusion, then identification capability is improved, but current increases when brushes are twined with hair, causing confusion with carpet operation
Solution Approach 1:
The patent extracts pure vibration signal information from the accelerometer, separating it from motor current measurements that are contaminated by brush-related interference. By relying solely on acceleration data rather than combining it with current measurements, the system eliminates the reliability problem of confusing brush entanglement signals with carpet operation signals.
Solution Approach 2:
The patent segments the analysis to focus only on the vibration characteristics of the robot body as measured by the accelerometer, isolating this signal from other system parameters such as motor current. This segmentation allows the system to identify floor materials based on vibration patterns alone, avoiding the reliability issues that arise from combining multiple sensors with overlapping or conflicting information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves low-cost, reliable, and accurate floor material identification, enabling the robot to adjust cleaning modes and suction force based on material type, improving stability and reliability.
Implementation Method 1
acquiring a vibration signal generated by a cleaning robot when operating on a floor of a material among a plurality of materials
Implementation Method 2
determining at least one identification parameter for floor material identification based on the vibration signal... determining low frequency band energy and high frequency band energy of the vibration signal
Data Source
AI summary
Provided are a floor material identification method, system and device, and a storage medium. The floor material identification method the floor material identification method includes: acquiring a vibration signal generated by a cleaning robot when operating on a floor of a material of a plurality of materials; determining at least one identification parameter for floor material identification based on the vibration signal; and identifying a floor material type based on the at least one identification parameter. Floor materials are distinguished by characteristics of different vibration signals from the floor of different materials, and thus problems such as high cost, low reliability and inaccurate identification result of the existing floor material identification method can be solved.