Mobile robot performing cliff detection
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Solution Overview
Problem
Current cliff detection methods in mobile robots, such as mechanical and ultrasonic sensors, are inadequate for reliably identifying cliffs and preventing falls.
Innovation Solution
A mobile robot equipped with a light source, an image sensor, and a processor that calculates pixel statistics by comparing bright and dark image frames captured when the light source is turned on and off, respectively, to determine if a cliff is present.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical sensors or ultrasonic sensors are used for cliff detection, then the robot can detect cliffs, but the detection reliability is insufficient and false positives/negatives occur
Solution Approach 1:
The patent combines multiple detection approaches (optical flow analysis from image sequences, pixel brightness comparison between light-on and light-off periods, and cliff detection algorithms) into a unified cliff detection system. This integration of multiple sensing modalities and processing methods resolves the contradiction by achieving both high reliability through redundancy and high precision through complementary measurement techniques.
Solution Approach 2:
The image sensor serves multiple functions: capturing optical flow information for motion compensation, detecting pixel brightness changes for cliff identification, and providing visual data for navigation. This multi-functionality resolves the contradiction by using a single component for multiple detection purposes, improving reliability through cross-validation while maintaining precision through specialized processing for each function.
2Reliability
If the image sensor captures multiple image frames continuously, then the robot can perform motion compensation and cliff detection, but the energy consumption increases
Solution Approach 1:
The patent implements periodic illumination with the light source, alternating between light-on and light-off periods, while capturing image frames at corresponding intervals. This periodic action enables the system to perform cliff detection by comparing pixel statistics between illuminated and dark periods while managing energy consumption through controlled, intermittent operation rather than continuous high-power operation.
Solution Approach 2:
The system performs motion compensation calculations using optical flow analysis from captured image frames before proceeding to cliff detection. This preliminary action of compensating for robot motion ensures accurate cliff detection results while optimizing energy usage by preparing the data in advance, reducing the need for repeated high-energy capture cycles.
3Reliability
If the robot performs cliff detection at all times, then cliff safety is ensured, but the processing time and computational load increase
Solution Approach 1:
The system uses the image sensor data captured for navigation and motion compensation purposes to simultaneously perform cliff detection without requiring separate dedicated detection operations. This self-service approach resolves the contradiction by utilizing already-acquired data for multiple purposes, ensuring continuous cliff monitoring reliability while minimizing additional processing time and computational overhead.
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 method effectively identifies cliffs by analyzing differences in pixel statistics, allowing the robot to take preventive measures and improve user safety.
Implementation Method 1
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
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.