A Large-Area Flexible Tactile Sensor for Multi-Touch and Force Detection Using Electrical Impedance Tomography

For large area robot skin design, the distribution of rigid components and wires in traditional array sensors leads to the decrease of the flexibility and extensibility of sensors. The flexible sensors based on non-invasive electrical impedance tomography (EIT) can avoid these shortcomings. However, design of a large-area flexible tactile sensors using EIT technique is still challenging due to the tradeoff between manufacturability and spatial resolution. In this study, we proposed a novel non-array EIT-based tactile sensor which is made of a porous elastic polymer and ionic liquid. The sensor free from internal array electrodes is straightforward to manufacture and can cover a large area with low cost. To improve the spatial resolution and touch sensitivity, we adopted a deep learning scheme Pyramid Scene Parsing Network (PSPNet) to postprocess the originally reconstructed images to enhance the sensor tactile perception. With this data-driven method, we achieved a single-point position detection error of 7.5± 4.5 mm without using internal electrodes. To overcome the location dependency of EIT sensing problems, we proposed a method of sub-regional fitting to calibrate the distributed forces for the large-area flexible tactile sensor and obtained a quantitative relationship between the touch force and EIT measurement for the entire sensing area for continuous sensing. The real-time performance of the proposed prototype sensor system demonstrated that we can achieve more accuracy of multi-touch and distributed force detection on the non-array sensors with a temporal resolution of about 1.3 frames/sec for potential applications in human-robot interfaces.