Mobile robot and cliff detection method thereof
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Solution Overview
Problem
Current cliff detection methods in mobile robots, such as mechanical and ultrasonic sensors, are inadequate for accurately identifying cliffs and preventing falls, as they lack precision and can be affected by environmental factors.
Innovation Solution
A mobile robot equipped with a light source, image sensor, and processor that calculates pixel statistics by comparing bright and dark image frames captured when the light source is turned on and off, using gray level, image quality, and shutter differences to identify cliffs, triggering a cliff detection mode when the moving speed drops below a preset threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mechanical sensors or ultrasonic sensors are used for cliff detection, then the robot can detect obstacles, but the detection precision and reliability are insufficient
Solution Approach 1:
The patent replaces mechanical sensors and ultrasonic sensors with an optical sensing system consisting of a light source and image sensor. This substitution uses optical principles (light reflection and image capture) instead of mechanical contact or sound wave detection, thereby improving both detection precision and reliability for cliff identification
Solution Approach 2:
The patent changes the detection parameter from mechanical displacement or sound echo to optical intensity and image pixel statistics. By analyzing the difference in pixel gray levels between images captured when the light source is on and off, the system achieves more precise and reliable cliff detection
2Productivity
If the robot moves at preset speed, then navigation efficiency is maintained, but the robot cannot detect cliffs until speed reduction occurs
Solution Approach 1:
The patent implements preliminary cliff detection by continuously analyzing image frames during normal navigation. The system proactively identifies cliffs by detecting characteristic patterns in pixel statistics before the robot reaches the cliff edge, allowing early warning and prevention of falls while maintaining navigation efficiency
Solution Approach 2:
The patent establishes a feedback mechanism where image analysis results are continuously fed back to the navigation system. The pixel statistics from successive image frames provide real-time feedback about the environment, enabling the robot to detect cliffs during normal operation and adjust its path before encountering dangerous situations
3Measurement precision
If optical cliff detection is implemented, then detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent makes the image sensor serve multiple functions: it is used for both general navigation/environmental perception and specific cliff detection. By analyzing pixel statistics from the same image frames used for navigation, the system achieves cliff detection without adding dedicated separate sensors, thereby limiting the increase in device complexity
Solution Approach 2:
The patent introduces a light source as an intermediary to enhance the optical contrast between cliff and non-cliff areas. This intermediary element simplifies the detection task by creating distinct brightness patterns that are easier to analyze, reducing the complexity of the image processing algorithms required for accurate cliff detection
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 optical cliff detection method enhances the accuracy of cliff identification, allowing the robot to prevent falls by distinguishing between cliffs and other speed-reducing obstacles, improving user experience and robot safety.
Implementation Method 1
The light source is configured to illuminate the operation surface. The image sensor is configured to receive reflected light from the operation surface and generate image frames.
Implementation Method 2
The image sensor is configured to capture multiple bright image frames within the first interval using a first shutter, and capture multiple dark image frames within the second interval using a second shutter.
Data Source
AI summary
There is provided a mobile robot including a light source, an image sensor and a processor. The image sensor respectively captures bright image frames and dark image frames corresponding to the light source being turned on and turned off. The processor identifies that the mobile robot faces a cliff when a gray level variation between the bright image frames and the dark image frames is very small, and controls the mobile robot to stop moving forward continuously.