Moving Robot Voice Feedback Adaptation Based on Usage History
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Solution Overview
Problem
Existing moving robots primarily utilize speech recognition as a control input method, lacking the ability to provide interactive services and vary their responses based on usage time, frequency, and pattern, which limits user engagement and product preference.
Innovation Solution
Incorporating an input unit for speech recognition, an audio output unit for feedback, and a controller that adjusts voice feedback based on usage history, mission levels, and learning levels, enabling the robot to provide personalized interactions and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition is used merely as a control input means, then the robot can receive user commands, but the interaction capability and user engagement remain limited
Solution Approach 1:
The speech recognition system is extended from a simple control input mechanism to a multi-functional communication interface. The robot不仅能够 recognize commands for basic operations, but also engages in conversational interactions, provides information, and delivers personalized feedback, thereby transforming a single-function control system into a versatile communication platform that enhances both ease of operation and adaptability
2Device complexity
If the robot provides fixed voice feedback, then the system is simple to implement, but user engagement and preference are reduced
Solution Approach 1:
The voice feedback system transitions from a static, fixed-response mechanism to a dynamic, adaptive system. The robot adjusts its voice characteristics including tone, pitch, and style based on usage history, mission levels, and learning progress. This dynamic adaptation allows the feedback system to evolve with the robot's capabilities and user preferences, enhancing user engagement without requiring overly complex system architecture
Solution Approach 2:
The feedback system utilizes parameter changes in voice characteristics such as tone, pitch, and volume to convey different information and emotional states. By varying these acoustic parameters based on usage context and learning level, the system provides diverse and engaging feedback that adapts to different situations and user preferences, maintaining simplicity while enhancing versatility
3Device complexity
If the robot lacks usage history tracking, then the system is simpler, but personalized interaction and user preference adaptation are impossible
Solution Approach 1:
The system implements feedback loops where usage history, mission completion data, and learning progress are continuously tracked and fed back to the control system. This feedback mechanism enables the robot to adapt its voice feedback and interaction style based on accumulated experience, achieving personalized interaction. The feedback principle allows the system to maintain relatively simple architecture while gaining sophisticated adaptation capabilities through data-driven adjustments
Data Source
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AI summary
The present disclosure relates to a moving robot including: an input unit configured to receive a speech input of a user; an audio output unit configured to output a feedback voice message corresponding to the speech input; a storage configured to store a usage history of the moving robot; and a controller configured to control the feedback voice message to be output in different voices according to the stored usage history, thereby providing different voice feedbacks according to the usage history.