Self-Powered Multimodal Tactile Sensing Enabled by Hybrid Triboelectric and Magnetoelastic Mechanisms - Xiao Lu; Tianhong Wang; Songyi Zhong; Tianqi Cao; Chenghao Zhou; Long Li; Quan Zhang; Shiwei Tian; Tao Jin; Tao Yue; Shaorong Xie (Cyborg and Bionic Systems)
Object property perception, as a core component of tactile sensing technology, faces severe challenges due to its inherent complexity and diversity, particularly under the constraints of decoupling difficulty and limited precision. Herein, this paper introduces an innovative approach to object property perception utilizing triboelectric–magnetoelastic sensing. This technology integrates triboelectricity and magnetoelasticity, achieving a self-powered sensing mechanism that requires no external power source for sensing signal generation. Moreover, by deploying a triboelectric array, it comprehensively captures multi-dimensional information of objects. Concurrently, in conjunction with magnetoelastic sensing technology, it provides stable and reliable mechanical information, ensuring that the system can accurately decouple key characteristics of objects, such as material properties, softness, and roughness, even in open environments where temperature, humidity, and mechanical conditions change in real time. Furthermore, by combining deep learning algorithms, it achieves exceptionally high recognition accuracy for object properties (material recognition accuracy: 99%, softness recognition accuracy: 100%, roughness recognition accuracy: 95%). Even in complex scenarios with intertwined multiple properties, the overall recognition accuracy remains consistently above 95%. The multimodal tactile sensing technology proposed in this paper provides robust technical support and theoretical foundation for the intelligent development of robots and the enhancement of real-time tactile perception capabilities.