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1. Soft capacitive tactile sensing arrays fabricated via direct filament casting

   https://doi.org/10.1088/0964-1726/25/7/075009  

   Li, Bin ; Gao, Yang ; Fontecchio, Adam ; Visell, Yon ( May 2016 , Smart Materials and Structures)
2. Skin-inspired textile-based tactile sensors enable multifunctional sensing of wearables and soft robots

   https://doi.org/10.1016/j.nanoen.2022.107137  

   Pang, Yaokun ; Xu, Xianchen ; Chen, Shoue ; Fang, Yuhui ; Shi, Xiaodong ; Deng, Yiming ; Wang, Zhong-Lin ; Cao, Changyong ( June 2022 , Nano Energy)
3. GradTac: Spatio-Temporal Gradient Based Tactile Sensing

   https://doi.org/10.3389/frobt.2022.898075  

   Ganguly, Kanishka ; Mantripragada, Pavan ; Parameshwara, Chethan M. ; Fermüller, Cornelia ; Sanket, Nitin J. ; Aloimonos, Yiannis ( June 2022 , Frontiers in Robotics and AI)

   Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck the right balance of anthropomorphic sizes and shapes and accuracy of tactile response measurement. However, this design is plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing mechanism “damping” the measurement values that are hard to model. To this end, we present a spatio-temporal gradient representation on the data obtained from fluid-based tactile sensors, which is inspired from neuromorphic principles of event based sensing. We present a novel algorithm (GradTac) that converts discrete data points from spatial tactile sensors into spatio-temporal surfaces and tracks tactile contours across these surfaces. Processing the tactile data using the proposed spatio-temporal domain is robust, makes it less susceptible to the inherent noise from the fluid based sensors, and allows accurate tracking of regions of touch as compared to using the raw data. We successfully evaluate and demonstrate the efficacy of GradTac on many real-world experiments performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors. Specifically, we use it for tracking tactile input across the sensor’s surface, measuring relative forces, detecting linear andmore »rotational slip, and for edge tracking. We also release an accompanying task-agnostic dataset for the BioTac SP, which we hope will provide a resource to compare and quantify various novel approaches, and motivate further research.« less
4. Improving Tactile Codes for Increased Speech Communication Rates in a Phonemic-Based Tactile Display

   https://doi.org/10.1109/TOH.2020.3008869  

   Martinez, Juan S. ; Tan, Hong Z. ; Reed, Charlotte M. ( January 2021 , IEEE Transactions on Haptics)
5. Tactile Behaviors with the Vision-Based Tactile Sensor FingerVision

   https://doi.org/10.1142/S0219843619400024  

   Yamaguchi, Akihiko ; Atkeson, Christopher G. ( June 2019 , International Journal of Humanoid Robotics)

   This paper introduces a vision-based tactile sensor FingerVision, and explores its usefulness in tactile behaviors. FingerVision consists of a transparent elastic skin marked with dots, and a camera that is easy to fabricate, low cost, and physically robust. Unlike other vision-based tactile sensors, the complete transparency of the FingerVision skin provides multimodal sensation. The modalities sensed by FingerVision include distributions of force and slip, and object information such as distance, location, pose, size, shape, and texture. The slip detection is very sensitive since it is obtained by computer vision directly applied to the output from the FingerVision camera. It provides high-resolution slip detection, which does not depend on the contact force, i.e., it can sense slip of a lightweight object that generates negligible contact force. The tactile behaviors explored in this paper include manipulations that utilize this feature. For example, we demonstrate that grasp adaptation with FingerVision can grasp origami, and other deformable and fragile objects such as vegetables, fruits, and raw eggs.