Search

One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume $3.5\mu \text{W}$ of power. The occupied silicon area is $180\mu \text{m}\,\,\times 220\mu \text{m}$ for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a set of novel tactile descriptors to enable robotic systems to extract robust tactile information during tactile object explorations, regardless of the number of the tactile sensors, sensing technologies, type of exploratory movements, and duration of the objects’ surface exploration. The performance and robustness of the tactile descriptors are verified by testing on four different sensing technologies (dynamic pressure sensors, accelerometers, capacitive sensors, and impedance electrode arrays) with two robotic platforms (one anthropomorphic hand and one humanoid), and with a large set of objects and materials. Using our proposed tactile descriptors, the Shadow Hand, which has multimodal robotic skin on its fingertips, successfully classified 120 materials (100% accuracy) and 30 in-hand objects (98% accuracy) with regular and irregular textural structure by executing human-like active exploratory movements on their surface. The robustness of the proposed descriptors was assessed further during the large object discrimination with a humanoid. With a large sensing area on its upper body, the humanoid classified 120 large objects with multiple weights and various textures while the objects slid between its sensitive hands, arms, and chest. The achieved 90% recognition rate shows that the proposed tactile descriptors provided robust tactile information from the large number of tactile signals for identifying large objects via their surface texture regardless of their weight. [ABSTRACT FROM AUTHOR]