Multimodal Strain Sensing System for Shape Recognition of Tensegrity Structures by Combining Traditional Regression and Deep Learning Approaches

A tensegrity-based system is a promising approach for dynamic exploration of uneven, unpredictable, and confined environments. However, implementing such systems presents challenges in state recognition. In this study, we introduce a 6-strut tensegrity structure integrated with 24 multimodal strain sensors, employing a deep learning model to achieve smart tensegrity. By using conductive flexible tendons and leveraging a long short-term memory (LSTM) model, the system accomplishes self-shape reconstruction without the need for external sensors. The sensors operate in two modes, and we applied both a curve fitting model and an LSTM model to establish the relationship between length change and resistance change in the sensors. Our key findings demonstrate that the intelligent tensegrity system can accurately self-detect and adapt its shape. Furthermore, a human pressing process allows users to monitor and understand the tensegrity's shape changes based on the integrated models. This intelligent tensegrity-based system with self-sensing tendons showcases significant potential for future exploration, making it a versatile tool for real-world applications.