LAUSR

Current research on grasping state analysis in soft manipulators is limited and lacks broad applicability. In this article, we introduce a novel method that leverages multimodal data from flexible sensors and inertial measurement units (IMUs) to develop a comprehensive grasping state analysis system for multidegree-of-freedom (multi-DOF) pneumatic soft manipulators. A deep spiking high-dimensional fuzzy network (DSHTFN) algorithm is specifically designed to analyze the “3S” grasping states of soft manipulators—shaking, stable, and slipping—with greater depth and precision. A novel membership function, the Bernoulli-Arctangent (B-Atan) function, has been designed to accommodate the unique characteristics of spiking input signals and support backpropagation capabilities. Experimental results demonstrate that our proposed method achieves accuracies of 95.66% and 96.05% in opposing-finger and three-fingered soft manipulator operations, respectively. Through comparative analysis with other algorithms, the superior performance of the B-Atan membership function and the DSHTFN approach in analyzing the grasping states of soft manipulators has been validated.