3,587 research outputs found
Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
Object manipulation actions represent an important share of the Activities of
Daily Living (ADLs). In this work, we study how to enable service robots to use
human multi-modal data to understand object manipulation actions, and how they
can recognize such actions when humans perform them during human-robot
collaboration tasks. The multi-modal data in this study consists of videos,
hand motion data, applied forces as represented by the pressure patterns on the
hand, and measurements of the bending of the fingers, collected as human
subjects performed manipulation actions. We investigate two different
approaches. In the first one, we show that multi-modal signal (motion, finger
bending and hand pressure) generated by the action can be decomposed into a set
of primitives that can be seen as its building blocks. These primitives are
used to define 24 multi-modal primitive features. The primitive features can in
turn be used as an abstract representation of the multi-modal signal and
employed for action recognition. In the latter approach, the visual features
are extracted from the data using a pre-trained image classification deep
convolutional neural network. The visual features are subsequently used to
train the classifier. We also investigate whether adding data from other
modalities produces a statistically significant improvement in the classifier
performance. We show that both approaches produce a comparable performance.
This implies that image-based methods can successfully recognize human actions
during human-robot collaboration. On the other hand, in order to provide
training data for the robot so it can learn how to perform object manipulation
actions, multi-modal data provides a better alternative
On the use of simulated experiments in designing tests for material characterization from full-field measurements
The present paper deals with the use of simulated experiments to improve the design of an actual mechanical test. The analysis focused on the identification of the orthotropic properties of composites using the unnotched Iosipescu test and a full-field optical technique, the grid method. The experimental test was reproduced numerically by finite element analysis and the recording of deformed grey level images by a CCD camera was simulated trying to take into account the most significant parameters that can play a role during an actual test, e.g. the noise, the failure of the specimen, the size of the grid printed on the surface, etc. The grid method then was applied to the generated synthetic images in order to extract the displacement and strain fields and the Virtual Fields Method was finally used to identify the material properties and a cost function was devised to evaluate the error in the identification. The developed procedure was used to study different features of the test such as the aspect ratio and the fibre orientation of the specimen, the use of smoothing functions in the strain reconstruction from noisy data, the influence of missing data on the identification. Four different composite materials were considered and, for each of them, a set of optimized design variables was found by minimization of the cost function
The 64 Mpixel wide field imager for the Wendelstein 2m Telescope: Design and Calibration
The Wendelstein Observatory of Ludwig Maximilians University of Munich has
recently been upgraded with a modern 2m robotic telescope. One Nasmyth port of
the telescope has been equipped with a wide-field corrector which preserves the
excellent image quality (< 0.8" median seeing) of the site (Hopp et al. 2008)
over a field of view of 0.7 degrees diameter. The available field is imaged by
an optical imager (WWFI, the Wendelstein Wide Field Imager) built around a
customized 2 2 mosaic of 4k 4k 15 \mu m e2v CCDs from
Spectral Instruments. This paper provides an overview of the design and the
WWFI's performance. We summarize the system mechanics (including a structural
analysis), the electronics (and its electromagnetic interference (EMI)
protection) and the control software. We discuss in detail detector system
parameters, i.e. gain and readout noise, quantum efficiency as well as charge
transfer efficiency (CTE) and persistent charges. First on sky tests yield
overall good predictability of system throughput based on lab measurements.Comment: 38 pages 19 Figures To be published in Springer Experimental
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