Apparatus and method for treating laundry
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Solution Overview
Problem
Conventional laundry treatment systems face challenges in accurately differentiating between pieces of laundry and sensing foreign substances due to reliance on visible light cameras and friction-based fabric sensing, which results in insufficient accuracy and longer data learning times.
Innovation Solution
The use of heterogeneous sensors, including 2D or 3D image sensors, ultrasonic sensors, radar, and LiDAR, to generate fusion sensing data for accurate laundry identification and control, allowing for simultaneous light and wave-based sensing to determine fabric type and motion characteristics, and employing machine learning or deep learning to process this data for improved washing cycle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a visible light camera is used to photograph laundry, then the system can capture images of laundry pieces, but the accuracy of differentiating between pieces of laundry is insufficient
Solution Approach 1:
The patent combines multiple sensing systems (visible light camera, infrared camera, and wave sensor) into a unified laundry detection system. The visible light camera captures color and texture information, the infrared camera detects thermal characteristics, and the wave sensor measures physical properties. By merging these heterogeneous sensing data, the system achieves comprehensive laundry identification that overcomes the limitations of any single sensor type, thereby improving differentiation accuracy without losing critical laundry information.
Solution Approach 2:
The patent introduces a new sensing dimension by adding wave-based sensing (ultrasonic or radar) to the traditional visual sensing approach. This wave sensor provides depth information, material density data, and three-dimensional structural characteristics that are inaccessible to conventional cameras. By transitioning from two-dimensional image capture to multi-dimensional sensing including wave propagation characteristics, the system achieves superior laundry piece differentiation and foreign substance detection.
2Measurement precision
If only an image sensor using visible light is used, then the system can detect laundry appearance, but foreign substances mixed with laundry cannot be easily sensed
Solution Approach 1:
The patent introduces wave sensors (ultrasonic or radar) as intermediary detection devices that can penetrate or reflect off foreign substances hidden within laundry loads. These wave-based sensors act as intermediaries between the detection system and concealed foreign objects, providing indirect measurement capabilities for materials that are invisible to visible light cameras. The wave sensors detect anomalies in wave propagation caused by foreign substances, enabling accurate detection without direct visual contact.
Solution Approach 2:
The patent applies different sensing modalities to different spatial regions and laundry characteristics. The visible light camera focuses on surface appearance and color, the infrared camera targets thermal signatures, and the wave sensor probes internal structure and density variations. By assigning specialized sensing functions to specific sensor types, the system creates localized detection expertise that collectively achieves comprehensive foreign substance detection capability.
3Measurement precision
If a personalized database based on fusion image using light sensing element and wave sensing element is used, then laundry information accuracy is improved, but much time and data are required for learning
Solution Approach 1:
The patent performs preliminary data processing and feature extraction during the sensing phase itself. The system pre-processes raw sensor data by extracting key characteristics (color histograms, thermal patterns, wave reflection coefficients) before storing them in the personalized database. This preliminary action reduces the dimensionality and complexity of stored data, enabling faster learning and classification during actual laundry detection without sacrificing accuracy. The fusion sensing data is pre-organized into meaningful features that accelerate subsequent machine learning operations.
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
This approach enables precise information collection about laundry, effectively identifying inappropriate items for washing and reducing data learning time, resulting in optimized washing cycles based on accurate fusion sensing data.
Implementation Method 1
a wave sensor including an ultrasonic sensor
Implementation Method 2
sensing data on a type of fabric by using scattering characteristics of a reflected wave of a wave sensor
Implementation Method 3
a wave sensor including radar
Implementation Method 4
a wave sensor including LiDAR
Implementation Method 5
a wave sensor including LiDAR
Data Source
AI summary
A method and an apparatus for treating laundry are disclosed. The method for treating laundry according to an embodiment of the present disclosure includes generating fusion sensing data on laundry by using a plurality of heterogeneous sensors, acquiring information about the laundry using the fusion sensing data, and controlling a washing cycle of the laundry based on the information about the laundry. According to the present disclosure, it is possible to collect accurate information about the laundry by using the fusion sensing data based on the heterogeneous sensors, and to control the washing cycle in a manner suitable for the laundry based on the collected information.