Robot Light Source Switching for Surface Material Identification
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Solution Overview
Problem
Cleaning robots lack the ability to effectively distinguish between different operation surfaces, such as solid and soft surfaces, leading to inadequate cleaning performance as they cannot adjust their operations accordingly.
Innovation Solution
A cleaning robot equipped with two light sources and an image sensor, where a processor switches between the light sources based on image quality to identify the surface material, allowing the robot to perform different cleaning modes suitable for various surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single light source is used for illumination, then the device structure is simple, but the robot cannot identify surface material
Solution Approach 1:
The illumination system is segmented into two distinct light sources: a first light source for general illumination and a second light source specifically for material identification. This segmentation allows each light source to serve its dedicated function, enabling surface material identification without significantly increasing overall system complexity.
Solution Approach 2:
The first light source serves dual purposes: it provides general illumination for navigation and obstacle detection, and also contributes to material identification when activated. This multi-functionality reduces the need for dedicated components, balancing identification capability with device simplicity.
2Object-affected harmful factors
If the robot uses liquid cleaning on all surfaces, then cleaning operation is simple, but it causes harm on soft surfaces like carpets
Solution Approach 1:
The robot employs a feedback mechanism where the image sensor continuously monitors surface material identification results, and the processor adjusts cleaning operations based on this feedback. When soft surfaces are detected, the system automatically prevents liquid discharge, avoiding damage while maintaining cleaning functionality on appropriate surfaces.
Solution Approach 2:
The cleaning operation is made dynamic and adaptive rather than static. The robot can switch between different cleaning modes (dry cleaning, wet cleaning, or no cleaning) based on real-time surface material identification, ensuring appropriate treatment for each surface type encountered.
3Measurement precision
If the robot switches light sources frequently, then material identification is accurate, but energy consumption increases
Solution Approach 1:
The second light source is activated periodically at predetermined time intervals rather than continuously, creating a rhythmic switching pattern. This periodic activation maintains material identification capability while significantly reducing energy consumption compared to continuous operation.
Solution Approach 2:
The system uses partial action by activating the second light source only when material identification is needed, rather than maintaining constant illumination. This selective activation provides sufficient identification accuracy while minimizing energy expenditure.
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
Enables the robot to accurately identify surface materials and adjust its cleaning operations, improving cleaning effectiveness and user experience by preventing liquid use on carpets and optimizing cleaning on solid surfaces.
Implementation Method 1
The image sensor is configured to capture reflected light from the operation surface to output an image frame
Data Source
AI summary
There is provided a robot including a first light source, a second light source, an image sensor and a processor. The processor is used to calculate an image quality of an image frame captured by the image sensor when the second light source is being turned on. The processor then determines whether to switch the second light source back to the first light source according to the image quality to accordingly identify the material of an operation surface.