2D tactile sensor based on multimode interference and deep learning

Abstract

A 2D tactile sensor is demonstrated using a single winding fiber embedded in a soft, elastic silicone substrate of 6 mm thickness. Laser light at 1550 nm is injected from a single mode fiber into a spliced multimode fiber and causes multimode interference. The formed image is susceptible to any disturbance along the fiber path. These images at the output facet are captured during the automatic scanning/probing loops by a spring needle. The collected data are fed into a convolutional neural network for training, validation and test. Results show that with only a few hundred images, over 98% accuracy can be achieved in recognizing the probe spatial position within a 0.5 mm × 0.5 mm resolution. In addition, close to 100% accuracy is reached for force sensing in the third dimension at a resolution of 3 g. The presented technology may lead to the design of large-area, fine-resolution yet low-cost touch pads, thanks to its extremely simple structure and powerful interrogation method.