2,214 research outputs found
Effects of Ground-State Correlations on High Energy Scattering off Nuclei: the Case of the Total Neutron-Nucleus Cross Section
With the aim at quantitatively investigating the longstanding problem
concerning the effect of short range nucleon-nucleon correlations on scattering
processes at high energies, the total neutron-nucleus cross section is
calculated within a parameter-free approach which, for the first time, takes
into account, simultaneously, central, spin, isospin and tensor nucleon-nucleon
(NN) correlations, and Glauber elastic and Gribov inelastic shadowing
corrections. Nuclei ranging from 4He to 208Pb and incident neutron momenta in
the range 3 GeV/c - 300 GeV/c are considered; the commonly used approach which
approximates the square of the nuclear wave function by a product of one-body
densities is carefully analyzed, showing that NN correlations can play a
non-negligible role in high energy scattering off nuclei.Comment: 5 pages, 3 figure
Slow Proton Production in Semi-Inclusive Deep Inelastic Scattering off Deuteron and Complex Nuclei: Hadronization and Final State Interaction Effects
The effects of the final state interaction in slow proton production in semi
inclusive deep inelastic scattering processes off nuclei, A(e,e'p)X, are
investigated in details within the spectator and target fragmentation
mechanisms; in the former mechanism, the hard interaction on a nucleon of a
correlated pair leads, by recoil, to the emission of the partner nucleon,
whereas in the latter mechanism proton is produced when the diquark, which is
formed right after the visrtual photon-quark interaction, captures a quark from
the vacuum. Unlike previous papers on the subject, particular attention is paid
on the effects of the final state interaction of the hadronizing quark with the
nuclear medium within an approach based upon an effective time-dependent cross
section which combines the soft and hard parts of hadronization dynamics in
terms of the string model and perturbative QCD, respectively. It is shown that
the final state interaction of the hadronizing quark with the medium plays a
relevant role both in deuteron and complex nuclei; nonetheless, kinematical
regions where final state interaction effects are minimized can experimentally
be selected, which would allow one to investigate the structure functions of
nucleons embedded in the nuclear medium; likewise, regions where the
interaction of the struck hadronizing quark with the nuclear medium is
maximized can be found, which would make it possible to study non perturbative
hadronization mechanisms.Comment: 35 pages, 12 figures, accepted for pubblication in Phys. Rev.
Combined joint-cartesian mapping for simultaneous shape and precision teleoperation of anthropomorphic robotic hands
There are many applications involving robotic hands in which teleoperation-based approaches are preferred to autonomous solutions. The main reason is that cognitive skills of human operators are desirable in some task scenarios, in order to overcome limitations of robotic hands abilities in dealing with unstructured environments and/or unpredetermined requirements. In particular, in this work we focus on the use of anthropomorphic grasping devices and, specifically, on their teleoperation based on movements of the human operator's hand (the master hand.) Indeed, the mapping of human hand configurations to an anthropomorphic robotic hand (the slave device) is still an open problem, because of the presence of dissimilar kinematics between master and slave that produce shape and/or Cartesian errors - as addressed within our study. In this work, we propose a novel algorithm that combines joint and Cartesian mappings in order to enhance the preservation of both finger shapes and fingertip positions during the teleoperation of the robotic hand. In particular, a transition between the joint and Cartesian mappings is realized on the basis of the distance between the fingertip of the master hands' thumb and the opposite fingers, in which the mapping of the thumb fingertip is specifically addressed. The result of the testing of the algorithm with a ROS-based simulator of a commercially available robotic hand is reported, showing the effectiveness of the proposed mapping
Experimental evaluation of synergy-based in-hand manipulation
In this paper, the problem of in-hand dexterous manipulation has been addressed on the base
of postural synergies analysis. The computation of the synergies subspace able to represent grasp and
manipulation tasks as trajectories connecting suitable configuration sets is based on the observation of
the human hand behavior. Five subjects are required to reproduce themost natural grasping configuration
belonging to the considered grasping taxonomy and the boundary configurations for those grasps that
admit internal manipulation. The measurements on the human hand and the reconstruction of the human
grasp configurations are obtained using a vision-based mapping method that assume the kinematics
of the robotic hand, used for the experiments, as a simplified model of the human hand. The analysis
to determine the most suitable set of synergies able to reproduce the selected grasps and the relative
allowed internal manipulation has been carried out. The grasping and in-hand manipulation tasks have
been reproduced bymeans of linear interpolation of the boundary configurations in the selected synergies
subspace and the results have been experimentally tested on the UB Hand IV
Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation
Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion
Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control
Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, however showing several limitations in realizing multiple grasping motion control. In this letter, we propose a novel Human-Robot interface (HRi) based on self-supervised regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out, consisting of an offline session for systematic evaluations and comparisons with traditional unsupervised approaches, and an online session for assessing real-time control of a wearable anthropomorphic robot hand. The offline results demonstrate that the proposed self-supervised regression approach overcame traditional unsupervised methods, even considering different robot hands with dissimilar kinematic structures. Furthermore, the subjects were able to successfully perform online control of multiple grasping motions of a real wearable robot hand, reporting for high reliability over repeated grasp-transportation-release tasks with different objects. Statistical support is provided along with experimental outcomes
Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation
Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion
RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation
Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time
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