Robot capable of detecting dangerous situation using artificial intelligence and method of operating the same
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Solution Overview
Problem
Conventional robots in airports and multiplexes cannot actively detect dangerous situations and require user input to notify management of hazards, leading to unhandled emergencies until capable personnel arrive.
Innovation Solution
A robot equipped with artificial intelligence using voice and image recognition models, based on deep learning algorithms, to automatically detect dangerous situations and take appropriate actions, such as notifying authorities or changing routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robots are used in airports and multiplexes, then they can provide basic services such as guidance information, but they cannot actively detect dangerous situations and only notify management offices by user input
Solution Approach 1:
The robot system is segmented into multiple functional modules: voice recognition module, image recognition module, deep learning processing module, and notification module. Each module handles specific aspects of dangerous situation detection, allowing the complex function to be distributed across specialized components that can be developed and maintained independently.
Solution Approach 2:
The robot performs preliminary detection actions by continuously monitoring the environment through voice and image recognition before dangerous situations escalate. The deep learning models are pre-trained to recognize patterns indicating potential hazards, enabling the robot to proactively identify and report dangerous situations before they require human intervention.
2Loss of time
If users manually input notifications of dangerous situations, then the robot system remains simple, but dangerous situations are left unhandled until capable personnel arrive
Solution Approach 1:
The robot performs self-service by autonomously detecting dangerous situations through its integrated sensors and AI processing capabilities. The system automatically analyzes voice and image data, identifies hazardous conditions, and generates notifications without requiring human users to manually report issues, thereby reducing response time and eliminating dependency on user awareness or action.
Solution Approach 2:
The robot implements continuous feedback loops where sensor data from microphones and cameras is constantly fed into the deep learning models, which process the information and generate real-time assessments of environmental safety. This feedback mechanism enables the robot to immediately detect and respond to changing conditions, providing timely notifications of dangerous situations.
3Measurement precision
If voice and image recognition models based on deep learning are implemented, then automated dangerous situation detection is achieved, but computational requirements and processing complexity increase
Solution Approach 1:
The computational workload is segmented between edge processing in the robot and cloud-based processing. The robot's onboard processors handle initial data acquisition and preprocessing, while more computationally intensive deep learning inference is performed remotely or in batches, reducing the robot's energy consumption while maintaining high recognition accuracy through access to powerful external computing resources.
Data Source
AI summary
A robot for detecting a dangerous situation using artificial intelligence includes a memory configured to store a voice recognition model for inferring whether a current situation is the dangerous situation from voice data and an image recognition model for inferring whether the current situation is the dangerous situation from image data, and a processor configured to acquire one or more of the voice data or the image data and output a notification indicating the dangerous situation when the dangerous situation is detected from the voice data using the voice recognition model or when the dangerous situation is detected from the image data using the image recognition model.