Texture recognition and localization in amorphous robotic skin

We present a soft robotic skin that can recognize and localize texture using a distributed set of sensors and computational elements that are inspired by the Pacinian corpuscle, the fast adapting, uniformly spaced mechanoreceptor with a wide receptive field, which is responsive to vibrations due to rubbing or slip on the skin. Tactile sensing and texture recognition is important for controlled manipulation of objects and navigating in one's environment. Yet, providing robotic systems or prosthetic devices with such capability at high density and bandwidth remains challenging. Each sensor node in the presented skin is created by collocating computational elements with individual microphones. These nodes are networked in a lattice and embedded in EcoFlex rubber, forming an amorphous medium. Unlike existing skins consisting of passive sensor arrays that feed into a central computer, our approach allows for detecting, conditioning and processing of tactile signals in-skin, facilitating the use of high-bandwidth signals, such as vibration, and allowing nodes to respond only to signals of interest. Communication between nodes allows the skin to localize the source of a stimulus, such as rubbing or slip, in a decentralized manner. Signal processing by individual nodes allows the skin to estimate the material touched. Combining these two capabilities, the skin is able to convert high-bandwidth, spatiotemporal information into low-bandwidth, event-driven information. Unlike taxel-based sensing arrays, this amorphous approach greatly reduces the computational load on the central robotic system. We describe the design, analysis, construction, instrumentation and programming of the robotic skin. We demonstrate that a 2.8 square meter skin with 10 sensing nodes is capable of localizing stimulus to within 2 centimeters, and that an individual sensing node can identify 15 textures with an accuracy of 71%. Finally, we discuss how such a skin could be used for full-body sensing in existing robots, augment existing sensing modalities, and how this material may be useful in robotic grasping tasks.