We describe a biomimetic tactile sensor that is sensitive to the wide range of normal and shear forces encountered in robotic and prosthetic applications: Figure 1. It is intrinsically simple, robust, and easy to manufacture and repair. The elastomeric skin is resistant to wear, and possesses texture and tackiness similar to the properties of human skin that facilitate grip. The curved, deformable nature of biological finger tips provides mechanical features that are important for the manipulation of the wide variety of objects encountered naturally. Electrodes are distributed along the surface of the rigid core and all sensitive components are safely embedded within the core. By applying an alternating current to each contact, one can measure the impedance of each volumetric flow path from a given contact to a reference electrode. Several factors will affect the resting impedance of an electrode: electrode size, material, fill volume, skin geometry, excitation frequency and fluid resistivity.;Because the conductivity of the fluid or gel increases with temperature, a thermistor is incorporated for thermal compensation. Furthermore, the fluid can be heated; when objects contact the device heat will be transferred according to the thermal and geometric properties of the object. Thus material information about the object can be extracted using heat-flow information, as biological finger tips do.;A hydrophone (pressure sensor) can also be mounted to the fluid channel of the sensor to gather acoustic information about contacted objects. Objects that slip will produce a high-frequency stick-slip phenomenon between the skin and the object; these high-frequency vibrations will be transmitted through the fluid and can be measured by the hydrophone. Objects with textures and surface features finer than the resolution of the impedance sensors will also produce a similar acoustic phenomenon within the fluid as the sensor haptically explores objects (Fishel et al. 2008). We posit material information from texture can be gathered from these data as well.;Here we will show that it is necessary to possess all three sensing modalities in order to make an accurate assessment of object properties. For example, if heat-flow sensing is used to gather information about a contacted object's thermal properties, one must calibrate the data with the force sensing modality, because surface area of contact, point of contact, object geometry and time of contact all impact the heat-flow signature.;Structure of dissertation. This discussion is separated into six chapters. In chapter one we outline the specific problems we are trying to solve in tactile sensing, the state of the art and the requirements to solve those problems. In chapter two we discuss the decision making process regarding the material choices for the core, skin and fluid and how these choices lend to meeting the requirements and constraints outlined in chapter one. Chapter three continues the discussion by taking these material constraints and demonstrating how design considerations evolved into the fabrication and testing practices that produced simple prototypes to the current sensors ready to be equipped on mechatronic manipulanda. Chapter four explores the notion of how to use the data produced by the sensor's force detection modality. This chapter focuses on how machine learning and heuristics can be used to extract radius of curvature, point of application of force and explicit force vectors. This is done not only match commercial sensor usage, but to show that these data are in fact embedded in the non-linear processes of the BioTac. Chapter five is a validation of sensor performance on a prosthetic hand. This involves normal and tangential force extraction using a Kalman filter for a constrained grip control task. Chapter six consists of preliminary experiments to thermally characterize objects using the heat-flow sensing modality. The paper ends with a discussion of how this work fits into the field of haptics and how the tactile signals can be used for conscious feedback of sensation for a prosthetic, reflexive grip control for a mechatronic hand and object property identification algorithms for robotic systems. (Abstract shortened by UMI.)
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