Customized Service Robot With Multi-Model Situation Recognition
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Solution Overview
Problem
Current interactive robots struggle to accurately recognize a user's situation and provide appropriate services actively, relying mainly on passive interactions and lacking integration of various sensing data for personalized care, especially for elderly individuals who require continuous care.
Innovation Solution
A robot equipped with a moving device, collection device, and processor that uses visual sensors and multiple analysis models to capture and process user data, integrating information to provide customized services based on the user's situation, utilizing both rule-based and machine learning models for accurate recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses only simple computing devices and passive interaction methods, then the device complexity is low, but the ability to accurately recognize user situation and provide appropriate service deteriorates
Solution Approach 1:
The robot system segments the situation recognition process into multiple independent analysis models, each specializing in different aspects: visual information processing, sensing data analysis, and user behavior pattern recognition. This segmentation allows each model to focus on specific features, improving overall recognition accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The robot merges multiple types of sensing data (visual, auditory, tactile, environmental sensors) into a unified situation recognition framework. By combining heterogeneous data sources and integrating them through the processor, the system achieves comprehensive user situation awareness that surpasses any single sensor type, resolving the contradiction between reliability improvement and complexity management
2Measurement precision
If the robot uses multiple analysis models and integrates various sensing data, then the user situation recognition accuracy is improved, but the device complexity increases
Solution Approach 1:
The processing system is segmented into specialized analysis models that each handle specific types of data and recognition tasks. This segmentation enables parallel processing of different sensing data streams, improving measurement precision through focused analysis while reducing the cognitive load on any single processing component, thus managing overall system complexity
Solution Approach 2:
The system performs preliminary processing and filtering of sensing data before full analysis. Raw data from multiple sensors undergoes initial validation, noise filtering, and relevance assessment before being fed into the main analysis models. This preliminary action reduces the complexity of subsequent processing by eliminating irrelevant data early in the pipeline
3Ease of operation
If the robot provides passive services only when users input requests, then the ease of operation is high, but the ability to actively provide appropriate care services deteriorates
Solution Approach 1:
The robot implements continuous feedback loops where sensing data and user responses are constantly monitored and fed back into the analysis models. This feedback mechanism enables the robot to dynamically adjust its service provision strategy, maintaining ease of operation through natural interaction while improving productivity by proactively responding to detected user needs without waiting for explicit requests
4Ease of manufacture
If the robot uses fixed determination criteria for situation recognition, then the ease of manufacture is high, but the likelihood of misrecognizing user situation increases
Solution Approach 1:
The determination criteria in the analysis models are designed to be dynamic rather than fixed. The system adapts its recognition thresholds and decision boundaries based on learned user patterns and contextual information. This dynamic adjustment improves situation recognition reliability by accommodating individual user variations while maintaining ease of manufacture through parameter-based flexibility rather than complex custom logic
Data Source
AI summary
A customized service providing robot according to an embodiment of the present disclosure includes: a moving device configured to move the robot to a location where a user is recognized; a collection device configured to capture an image of the user through a visual sensor and collect imaging information; and a processor configured to process the information collected by the collection device and control the moving device, and the processor inputs the information collected through the collection device into at least one pre-trained analysis model to derive element information for each analysis model (a), integrates the derived element information for each analysis model to generate integrated information and recognizes the user's current situation based on the integrated information to generate situation information (b), and provides a customized service to the user based on the situation information (c).