Dirtiness level determining method and robot cleaner applying the dirtiness level determining method
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Solution Overview
Problem
Conventional auto clean machines lack a method to automatically detect the dirtiness level of their image sensors, which can lead to frequent manual cleaning or operational issues due to sensor contamination.
Innovation Solution
A method and system for a robot cleaner to automatically determine the dirtiness level of its image sensor by capturing images at different times or of a reference surface, calculating a fixed pattern difference, and generating a notification if the dirtiness level exceeds a threshold, utilizing one or multiple light sources and a control circuit to assess and report the sensor's condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the image sensor is used for tracking location over time, then the tracking function is improved, but the image sensor becomes dirty and affects tracking performance
Solution Approach 1:
The system performs preliminary detection of sensor dirtiness by capturing images and analyzing fixed patterns before the dirtiness severely impacts tracking performance. This allows early intervention through notifications to users, preventing operational failures.
Solution Approach 2:
The system establishes a feedback loop where captured images are continuously analyzed to detect fixed patterns indicating sensor dirtiness. Based on this feedback, the system generates notifications to users, enabling them to clean the sensor before tracking performance deteriorates.
2Reliability
If manual cleaning is performed frequently to maintain sensor cleanliness, then tracking reliability is improved, but user time and operational complexity increase
Solution Approach 1:
The system enables self-service monitoring of sensor condition through automatic image capture and fixed pattern analysis. The sensor's own captured images are used to detect its own dirtiness level, eliminating the need for frequent manual inspection and cleaning by users.
Solution Approach 2:
The system replaces manual mechanical cleaning operations with an automated optical detection system. Instead of requiring users to physically clean the sensor frequently, the system uses image processing and fixed pattern analysis to monitor sensor condition and notify users only when cleaning is actually needed.
3Device complexity
If no detection system is implemented, then device complexity is reduced, but operational failures increase due to undetected sensor dirtiness
Solution Approach 1:
The system uses the existing image sensor for dual purposes: both for its primary tracking function and for self-diagnosis of its own dirtiness condition. The same image capture capability is reused to detect fixed patterns in captured images, indicating sensor contamination, thereby avoiding the need for separate dedicated detection hardware.
Solution Approach 2:
The system creates a digital copy of the sensor's own imaging capability by analyzing fixed patterns in captured images. This allows the sensor to effectively monitor its own condition through image processing, enabling reliability monitoring without adding physical detection components.
Data Source
AI summary
A dirtiness level determining method, applied to a robot cleaner comprising an image sensor, comprising: capturing an image of a reference surface as a reference image: capturing a current image; calculating a fixed pattern according to a difference between the reference image and the current image; calculating a dirtiness level of the image sensor according to the fixed pattern; and generating a notifying message if the dirtiness level is higher than a dirtiness threshold. The dirtiness level of the image sensor can be automatically determined by the robot cleaner, thus the user can be notified before the auto clean machine cannot normally operate.