Service Robot User Identification via Visual Service Tokens
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Solution Overview
Problem
Existing robots lack the ability to identify users who have requested services and provide tailored services to them, relying solely on pre-set routes and server commands without user recognition.
Innovation Solution
A robot equipped with an input interface, camera, and processor that uses an object recognition model based on an artificial neural network to identify users through video analysis and perform tasks corresponding to service requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot moves according to a set route or control server settings, then the robot can provide guide service, but the robot cannot identify users who have requested service or provide tailored services to them
Solution Approach 1:
The system is divided into multiple functional modules: a user identification module that analyzes video to detect service identifiers, a service information acquisition module that retrieves data based on identified identifiers, and a task execution module that performs customized services. This segmentation allows the robot to add user identification capabilities without completely redesigning the system architecture.
Solution Approach 2:
The patent introduces a service identifier as an intermediary element that bridges the user and the robot. The identifier (such as a QR code or RFID tag) carries service information that the robot can read and process, enabling customized service delivery without requiring complex direct user-robot communication systems.
2Measurement precision
If the robot uses video analysis with object recognition model to identify users, then user identification accuracy is enhanced, but processing time and computational resources increase
Solution Approach 1:
The object recognition model is pre-trained with service identifier data before deployment. When a user presents a service identifier, the model can quickly match it against stored patterns without requiring extensive real-time processing. This preliminary preparation significantly reduces identification time while maintaining high accuracy.
Solution Approach 2:
The video analysis focuses specifically on detecting service identifiers rather than performing comprehensive user authentication. The system processes only the necessary visual information (the identifier itself) rather than analyzing all user characteristics, reducing computational overhead while achieving the required identification accuracy.
Data Source
AI summary
A robot including an input interface configured to receive a service identifier in response to a service request; a camera configured to capture a video of a user who has presented the service identifier; a transceiver configured to receive service information associated with the service identifier; and a processor configured to identify the user who has presented the service identifier from the video using an artificial neural network based learning model; and in response to successfully identifying the user, perform a task corresponding to the service request for the user based on the service information.