Cleaning robot projecting different light patterns
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Solution Overview
Problem
Conventional cleaning robots can only detect one-dimensional depth information and often bump into obstacles due to dead zones between different sensors, leading to noise, damage, and reduced service lifetime.
Innovation Solution
A cleaning robot equipped with a first and second diffractive optical element, light sources, and an image sensor that projects line and speckle patterns to capture two-dimensional depth information, allowing the robot to detect obstacles and calculate wall distances using a single image sensor, eliminating the need for multiple sensors and reducing dead zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple different sensors are used to detect front distance and wall distance, then detection coverage is improved, but dead zones between sensors cause frequent bumping into obstacles
Solution Approach 1:
The patent combines multiple detection functions (front distance detection and wall distance detection) into a single image sensor by projecting different light patterns (line pattern for depth, speckle pattern for shape). This merging eliminates dead zones between separate sensors and prevents frequent bumping into obstacles while maintaining comprehensive detection coverage.
Solution Approach 2:
The image sensor is designed to perform multiple functions: detecting front obstacles using line patterns, identifying obstacle appearance using speckle patterns, and calculating wall distances. This multi-functionality replaces multiple specialized sensors, eliminating detection gaps and improving reliability.
2Device complexity
If conventional sensors are used for obstacle detection, then simple detection is achieved, but two-dimensional depth information and obstacle appearance cannot be identified
Solution Approach 1:
The patent transitions from one-dimensional depth detection to two-dimensional depth information acquisition by using an image sensor with line and speckle light patterns. This enables the system to identify both depth and obstacle appearance simultaneously, providing comprehensive spatial information without significantly increasing system complexity.
3Measurement precision
If multiple sensors are deployed for comprehensive detection, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent merges multiple sensor functions into a single image sensor that can detect front obstacles, identify their appearance, and calculate wall distances by processing different light patterns. This consolidation maintains high detection accuracy while reducing device complexity and eliminating the need for multiple separate sensors.
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
The robot effectively detects and calculates distances from both front and side obstacles, including transparent ones, reducing bumping incidents and extending its service life by using a single image sensor to capture and process pattern images for accurate depth and shape identification.
Implementation Method 1
a first diffractive optical element, a first light source... The first light source is configured to project a line pattern through the first diffractive optical element
Implementation Method 2
a second diffractive optical element, a second light source... The second light source is configured to project a speckle pattern through the second diffractive optical element, wherein the speckle pattern is for identifying an appearance of an obstacle
Data Source
AI summary
There is provided a cleaning robot including a light source module, an image sensor and a processor. The light source module projects a line pattern and a speckle pattern toward a moving direction. The image sensor captures an image of the line pattern and an image of the speckle pattern. The processor calculates one-dimensional depth information according to the image of the line pattern and calculates two-dimensional depth information according to the image of the speckle pattern.