Large Language Model-empowered multimodal strain sensory system for shape recognition, monitoring, and human interaction of tensegrity

TL;DR

This paper presents an intelligent, multimodal tensegrity system integrated with deep learning and large language models, enabling shape recognition, wireless monitoring, and human interaction for space exploration and dynamic environments.

Contribution

It introduces a novel tensegrity system with self-sensing tendons and AI integration for autonomous shape reconstruction, monitoring, and human communication.

Findings

Achieved self-shape reconstruction using LSTM-based sensors.

Enabled wireless data transmission and analysis via GPT-3.5.

Demonstrated human interaction through natural language explanations.

Abstract

A tensegrity-based system is a promising approach for dynamic exploration of uneven and unpredictable environments, particularly, space exploration. However, implementing such systems presents challenges in terms of intelligent aspects: state recognition, wireless monitoring, human interaction, and smart analyzing and advising function. Here, we introduce a 6-strut tensegrity integrate with 24 multimodal strain sensors by leveraging both deep learning model and large language models to realize smart tensegrity. Using conductive flexible tendons assisted by long short-term memory model, the tensegrity achieves the self-shape reconstruction without extern sensors. Through integrating the flask server and gpt-3.5-turbo model, the tensegrity autonomously enables to send data to iPhone for wireless monitoring and provides data analysis, explanation, prediction, and suggestions to human for decision making. Finally, human interaction system of the tensegrity helps human obtain necessary information of tensegrity from the aspect of human language. Overall, this intelligent tensegrity-based system with self-sensing tendons showcases potential for future exploration, making it a versatile tool for real-world applications.