  Tactile sensing is an essential perception for robots to complete dexterous
tasks. As a promising tactile sensing technique, vision-based tactile sensors
have been developed to improve robot performance in manipulation and grasping.
Here we propose a new design of a vision-based tactile sensor, DelTact. The
sensor uses a modular hardware architecture for compactness whilst maintaining
a contact measurement of full resolution (798*586) and large area (675mm2).
Moreover, it adopts an improved dense random color pattern based on the
previous version to achieve high accuracy of contact deformation tracking. In
particular, we optimize the color pattern generation process and select the
appropriate pattern for coordinating with a dense optical flow algorithm under
a real-world experimental sensory setting. The optical flow obtained from the
raw image is processed to determine shape and force distribution on the contact
surface. We also demonstrate the method to extract contact shape and force
distribution from the raw images. Experimental results demonstrate that the
sensor is capable of providing tactile measurements with low error and high
frequency (40Hz).
