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.
We present a soft, amorphous skin that can sense and localize textures. The skin consists of a series of sensing and computing elements that are networked with their local neighbors and mimic the function of the Pacinian corpuscle in human skin. Each sensor node samples a vibration signal at 1KHz, transforms the signal into the frequency domain, and classifies up to 15 textures using logistic regression. By measuring the power spectrum of the signal and comparing it with its local neighbors, computing elements can then collaboratively estimate the location of the stimulus. The resulting low-bandwidth information, consisting of the texture probability distribution and its location are then routed to a sink anywhere in the skin in a multi-hop fashion. We describe the design, manufacturing, classification, localization and networking algorithms and experimentally validate the proposed approach. In particular, we demonstrate texture classification with 71% accuracy and centimeter accuracy in localization over an area of approximately three square feet using ten networked sensor nodes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.