Robot Motion Control Using Sensor Clustering for Emotional Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot control systems fail to effectively mimic the expressive movements and emotional responses of pets, as they lack advanced sensing and clustering methods to interpret user interactions, leading to limited emotional expression and adaptability.
Innovation Solution
A robot control device that uses unsupervised clustering methods to classify input data from acceleration and angular velocity sensors, generating relationship data to control movement and emotional responses, allowing for more expressive and personalized interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robot control systems are used, then the device complexity is low, but the emotional expression capability and adaptability are limited
Solution Approach 1:
The robot performs unsupervised clustering autonomously to classify sensor data and generate emotional responses without external intervention. The system self-organizes the input data into clusters and automatically determines movement patterns based on cluster relationships, enabling the robot to serve itself in terms of emotional intelligence development.
Solution Approach 2:
The patent replaces traditional rule-based control mechanisms with data-driven clustering algorithms. Instead of pre-programmed mechanical decision-making, the system uses unsupervised learning methods to automatically categorize sensor inputs and generate appropriate emotional responses, substituting mechanical control with intelligent data processing.
2Adaptability or versatility
If unsupervised clustering methods are implemented, then the emotional expression and adaptability improve, but the computational processing requirements increase
Solution Approach 1:
The system applies clustering algorithms selectively to sensor data that requires emotional interpretation, rather than processing all possible data streams. By focusing computational resources on partial data sets that are most relevant to emotional expression, the system achieves adaptability without requiring excessive processing power for unnecessary computations.
3Ease of operation
If simple threshold-based control is used, then the system is easy to operate, but the emotional responses are limited and not lifelike
Solution Approach 1:
The robot autonomously performs clustering operations to organize sensor data and generate emotional responses without requiring complex external programming. This self-service approach maintains ease of operation while significantly improving emotional expression accuracy through intelligent data organization and pattern recognition.
Data Source
AI summary
An apparatus control device includes: at least one processor; and at least one first memory that stores a program executed by the processor, in which the processor acquires input data based on at least one of acceleration and angular velocity generated by application of an external force to an apparatus, classifies a plurality of the acquired input data into a plurality of clusters by an unsupervised clustering method, acquires relationship data representing relationship between the acquired input data and the plurality of classified clusters, and controls movement of the apparatus based on the acquired relationship data.