Sterilization Robot Control Using Cough Direction and Mask Detection
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Solution Overview
Problem
Existing sterilization robots require user operation to identify and sterilize droplet spraying points or contact points, making automated sterilization difficult.
Innovation Solution
A robot equipped with microphones, a camera, and a processor that can autonomously identify a sterilization area by detecting coughing sounds, identifying non-mask wearing users, and determining the scale and intensity of sterilization based on audio signal intensity and image analysis, using neural network models to guide the robot to perform targeted sterilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sterilization robots use user operation to identify droplet spraying points or contact points, then sterilization can be performed at targeted locations, but automated sterilization function cannot be achieved
Solution Approach 1:
The sterilization robot autonomously identifies coughing sounds through microphones, detects non-mask wearing users via camera, and automatically navigates to sterilization areas without requiring user operation. The system serves itself by independently completing the entire sterilization workflow from detection to execution.
Solution Approach 2:
The patent replaces manual user operation with automated sensing and processing systems. Microphones substitute for user auditory detection, cameras replace user visual identification, and neural network models eliminate manual decision-making, achieving full automation of the sterilization process.
2Extent of automation
If the robot uses multiple sensors and neural network models to autonomously identify sterilization areas, then automated sterilization is achieved, but device complexity increases
Solution Approach 1:
The robot integrates multiple sensors (microphones, camera, distance sensor) and neural network models into a single multi-functional system that performs audio detection, visual recognition, and navigation. This universal system handles all sterilization identification tasks through one coordinated platform, managing complexity through functional integration.
Solution Approach 2:
The processor acts as an intermediary that coordinates between multiple sensors and the control system. The neural network models serve as intermediaries between raw sensor data and sterilization area identification, simplifying the complex data processing pipeline through structured intermediate processing layers.
3Productivity
If the robot identifies sterilization area based on coughing sound intensity and user position, then targeted sterilization is achieved, but measurement precision requirements increase
Solution Approach 1:
The system uses feedback from neural network model predictions to continuously refine sterilization area identification. The processor analyzes audio signal intensity, camera image data, and user positions with feedback loops that adjust identification accuracy based on detected patterns and thresholds, maintaining high precision through adaptive processing.
Solution Approach 2:
The patent changes parameters such as audio signal intensity thresholds, camera field of view angles, and distance sensor ranges to optimize detection precision. The system adjusts these parameters dynamically based on environmental conditions and detected coughing sound characteristics, maintaining accurate sterilization targeting through parameter optimization.
Data Source
AI summary
Provided is a robot comprising a driving part, a camera, a plurality of microphones arranged in different directions. The robot further comprises a memory storing instructions, and a processor configured to execute the instructions to identify an originating direction of an audio signal input through the plurality of microphones based on the audio signal being identified as corresponding to a coughing sound, control the camera to capture an image in the originating direction, identify a sterilization area based on a position of a user who does not wear a mask identified in the image, and control the driving part to move the robot to the sterilization area.