Design and use of a biomimetic tactile microvibration sensor with human-like sensitivity and its application in texture discrimination using Bayesian exploration

摘要

The cutaneous sensing of microvibrations in human fingertips plays a central role in the detection of slip-related and dynamic information critical for tool usage, reflexive grip control, and the perception of microtextures. This is made possible by the Pacinian corpuscle, a small sensory receptor in subcutaneous tissues that is capable of detecting vibrations up to 1000Hz in frequency. These receptors are sensitive to vibrations less than a micrometer in amplitude at frequencies around their maximal sensitivity of 250Hz. Artificial systems seeking to provide human-like dexterity and perception would benefit from similar sensory capabilities. ? In this dissertation, a novel tactile sensor capable of robustly sensing vibrations with a bandwidth and sensitivity that exceeds human performance is presented. The device, known as the BioTac, has a biomimetic structure that consists of a rigid bone-like core covered with an elastomeric skin. The space between the skin and core is filled with an incompressible low-viscosity liquid that is in contact with a pressure transducer. Vibrations that originate on the surface of the skin are transmitted as sound waves through the liquid and are readily sensed by the transducer. The incompressible liquid conducts these acoustic signals with little attenuation, permitting the transducer to be located in a protected region inside the core of the device, where it is less likely to get damaged. The addition of a biologically inspired fingerprint-like pattern on the surface of the skin was found to enhance vibrations sensed by the BioTac. The BioTac exceeded human capabilities in sensitivity thresholds to applied sinusoidal vibrations and impacts from small spheres. ? Biologically inspired strategies to use this tactile information for a texture discrimination task were developed. A specialized robot was built to make sliding movements similar to those humans make when exploring textures. The BioTac was slid over a total of 117 different textured surfaces collected from art supply, fabric and hardware stores. Signal processing methods were developed to extract properties modeled after the descriptive language that humans use when describing textures (rough/smooth, sticky/slippery, coarse/fine). Different sliding exploratory movements (defined by a combination of contact force and sliding velocity) were found to be optimal for discriminating each of these properties. All 117 textures were tested repeatedly with these three exploratory movements to collect a database of prior experience similar to human memory. A novel process of intelligently selecting exploratory movements was developed to guide the discrimination task when presented with an unknown texture. When exploring a texture, the Bayesian exploration algorithm selects the optimal movement to make and the property to measure based on previous experience to disambiguate the most-probable candidates. The combination of biomimetic hardware and software achieved performance that matched (and even surpassed) human capabilities in discriminating and identifying textures.