1,721,060 research outputs found
Review of Assistive Strategies in Powered Lower-Limb Orthoses and Exoskeletons
No full textStarting from the early research in the 1960s, especially in the last two decades, orthoses and exoskeletons have been significantly developed. They are designed in different architectures to assist their users’ movements. The research literature has been more prolific on lower-limb devices: a main reason is that they address a basic but fundamental motion task, walking. Leg exoskeletons are simpler to design, compared to upper-limb counterparts, but still have particular cognitive and physical requirements from the emerging human–robot interaction systems. In the state of the art, different control strategies and approaches can be easily found: it is still a challenge to develop an assistive strategy which makes the exoskeleton supply efficient and natural assistance. So, this paper aims to provide a systematic overview of the assistive strategies utilized by active locomotion–augmentation orthoses and exoskeletons. Based on the literature collected from Web of Science and Scopus, we have studied the main robotic devices with a focus on the way they are controlled to deliver assistance; the relevant validations are as well investigated, in particular experimentations with human in the loop. Finally current trends and major challenges in the development of an assistive strategy are concluded and discussed
Editorial: Human-Like Advances in Robotics: Motion, Actuation, Sensing, Cognition and Control
Full text linkEndoVESPA: a computer-integrated robotic platform for magnetically-driven soft-tethered colonoscopy
No full textDecoding of naturalistic textures from spike patterns of neuromorphic artificial mechanoreceptors
No full textSoft-neuromorphic artificial touch for applications in neuro-robotics
No full textWe propose an artificial mechanotransduction system based on a 2×2 MEMS array touch sensor, and evaluate a neural model which is designed to convert raw sensor outputs into neural spike-trains. We show that core tactile information is preserved in the neural representation, and that the resulting modulation via spikes can be used in surface discrimination tasks. In this research study the first neural stage (i.e., at mechanoreceptor level) of somatosensory system was mimicked in a soft-neuromorphic fashion, while future works will target the implementation of the 2nd order stage (Cuneate neurons) to further understand the biological mechanisms underlying maximum transfer and fast processing of tactile information
Haptic-assistive technologies for audition and vision sensory disabilities
No full textThe aim of this review is to analyze haptic sensory substitution technologies for deaf, blind and deaf-blind individuals
Investigation on calibration methods for multi-axis, linear and redundant force sensors
No full textA new method for the calibration of multi-axis, linear and redundant force sensors is presented. This new procedure, named device hyperplane characterization method, is inspired by the shape from motion method for it reduces the burden represented by the need for a huge number of force measurements, typical using least-squares methods, in order to reject errors during the calibration procedure. The proposed technique is an application of the rank theorem and achieves good noise rejection without much time consumption focusing on sensor output measurements, and reducing the effect of disturbances operating the projection of raw output data on the hyperplane to which measurements are ideally compelled to belong in the case of redundant sensors
Roughness Encoding for Discrimination of Surfaces in Artificial Active-Touch
No full textA 2×2 array of four microelectromechanical system (MEMS) tactile microsensors is integrated with readout electronics in the distal phalanx of an anthropomorphic robotic finger. A total of 16 sensing elements are available in a 22.3-mm2 area (i.e., 72 units/cm2 ) of the artificial finger, thus achieving a density comparable with human Merkel mechanoreceptors. The MEMS array is covered by a polymeric packaging with biomimetic fingerprints enhancing the sensitivity in roughness encoding. This paper shows the ability of the sensor array to encode roughness for discrimination of surfaces, without requiring dedicated proprioceptive sensors for end-effector velocity. Three fine surfaces with 400-, 440-, and 480- μm spatial periods are quantitatively evaluated. Core experiments consisted in active-touch exploration of surfaces by the finger executing a stereotyped human-like movement. A time–frequency analysis on pairs of tactile array outputs shows a clustering of the fundamental frequency, thus yielding 97.6% worst-case discrimination accuracy with a k-nearest-neighbor (k-NN) classifier. Hence, surfaces differing down to 40 μm are identified in active-touch by both hardware and processing methods based on exteroceptive tactile information. Finally, active-touch results with five textiles (which differ in texture or orientation) are shown as a preliminary qualitative assessment of discrimination in a more realistic tactile-stimulation scenario
Editorial: Neurotechnologies in translation: technological challenges and entrepreneurship opportunities
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