Carpet detecting method for robot, robot obstacle avoidance method, robot, and chip
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Solution Overview
Problem
Current carpet identification methods for cleaning robots, such as ultrasonic and main brush current identification, suffer from slow response, low identification rates, and inability to detect carpet fiber length, necessitating improved detection and avoidance strategies.
Innovation Solution
Utilizing a line laser sensor for real-time carpet detection by plane scanning and determining obstacles with a height difference within a preset range as carpets, and incorporating a chip to execute carpet detection and obstacle avoidance methods, including calculating carpet and fiber heights for precise navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If ultrasonic identification or main brush current identification is used for carpet detection, then the robot can detect carpets, but the response speed is slow and the identification rate is low
Solution Approach 1:
The patent replaces mechanical detection methods (ultrasonic sensing and main brush current monitoring) with optical sensing using a line laser sensor. This substitution enables non-contact, real-time carpet detection by projecting laser lines and analyzing the reflected light patterns, thereby achieving both fast response speed and high identification accuracy without mechanical wear or slow signal processing
Solution Approach 2:
The patent changes the detection parameter from mechanical contact (brush current) or acoustic waves (ultrasonic) to optical parameters (laser line reflection patterns). By analyzing the deformation of projected laser lines on carpet surfaces, the system achieves rapid and accurate carpet identification, transforming the detection mechanism into a faster, non-contact optical measurement system
2Loss of information
If traditional carpet detection methods are used, then carpet detection is possible, but the ability to detect carpet fiber length is lost
Solution Approach 1:
The patent extends detection from two-dimensional surface presence to three-dimensional structural measurement by using the vertical height information from the line laser sensor. The sensor captures not only whether a carpet is present but also measures the height of carpet fibers by analyzing the elevation differences in the reflected laser lines, thereby recovering fiber length detection capability while maintaining high precision through optical measurement
3Measurement precision
If complex detection algorithms are used to improve identification accuracy, then detection precision improves, but computational burden increases
Solution Approach 1:
The patent extracts only the essential feature for carpet identification - the deformation pattern of projected laser lines - and processes only this specific optical signature. By focusing computational resources on analyzing the characteristic reflection patterns of laser lines on carpet surfaces rather than processing all environmental data, the system achieves high identification accuracy with reduced computational burden, isolating and processing only the relevant detection signal
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
Enhances carpet identification accuracy with rapid response and reduced computational burden, enabling effective obstacle avoidance based on carpet and fiber height differentiation.
Implementation Method 1
a line laser sensor configured to generate a line laser to detect an object; an image sensor configured to obtain a line laser image projected by the line laser sensor onto a surface of the object
Data Source
AI summary
Disclosed are a carpet detection method for a robot, an obstacle avoidance method, a robot, and a chip. The carpet detection method includes: Step S1, in a case that the robot has detected an obstacle with an uneven contour, performing plane scanning based on data from a line laser sensor; and Step S2, in a case that an unevenness degree of the contour is within a preset range, determining that the obstacle is a carpet based on data obtained after the plane scanning.