Location and Orientation Super-Resolution Sensing With a Cost-Efficient and Repairable Barometric Tactile Sensor

Abstract

The adoption of tactile sensors in robotics is hindered by their high cost and fragility. We designed and validated a cost-effective and robust barometric tactile sensor array, whose material cost is below 80 USD. Unlike past work, we do not mold the rubber surface over the barometers but instead keep it as a separate element, leading to a design that is easy to fabricate and repair. Machine learning techniques are applied to enhance the sensor's localization precision, increasing the effective resolution from 6 mm (the distance between adjacent barometers) to 0.284 mm. To investigate the localization model's robustness, we utilized an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E-TRoll</i> robotic gripper to roll differently shaped prismatic objects across the sensing surface mounted on one finger. Under these uncontrolled settings, we achieved a satisfactory average real-time localization resolution of within 2.66 mm. Furthermore, we demonstrate a novel practical application: The E-TRoll mimics a one-DoF parallel gripper inferring a cube's orientation relative to the sensor. The range of orientations is split into four classes, which a trained CNN-LSTM model can predict with an 86.91% five-fold cross-validated accuracy.