RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking

Radio Frequency Identification (RFID) technology is becoming very popular in the new era of Industry 4.0, especially for warehouse management, retails, and logistics. RFID systems can be used for objects identification, localization, and tracking, facilitating everyday operators' efforts. However, the deployment of RFID tags and reader antennas in real-world application scenarios is crucial and takes time. Indeed, deciding where to place tags and/or readers' requires examining many conditions. If some weaknesses appear in the design, the arrangement must be reconsidered. The proposed work presents a novel open-source RFID simulator that allows modeling environments and testing the deployment of RFID tags and antennas apriori. In such a way, validating the performance of the localization or tracking algorithms in simulation, possible weaknesses that could arise may be fixed before facilities are applied on the field. Any number of tags and antennas can be placed in any position in the created scenario, and the simulator provides the phase and the RSSI signals for each tag. Every reader antenna is parametrized so that different antennas of different vendors can be reproduced. The simulator is implemented as a plugin of Gazebo, a widely used robotic framework integrated with the Robot Operating System (ROS), to reach a broad audience. In order to validate the simulator, a warehouse scenario is modeled, and a tag localization algorithm that uses the phase unwrapping technique and hyperbolae intersection method employing a reader antenna mounted on a mobile robot is used to estimate the position of the tags deployed in the scenario. The outcomes of the experiments showed realistic results

Real-Time Numerical Simulation for Accurate Soft Tissues Modeling during Haptic Interaction

The simulation of fabrics physics and its interaction with the human body has been largely studied in recent years to provide realistic-looking garments and wears specifically in the entertainment business. When the purpose of the simulation is to obtain scientific measures and detailed mechanical properties of the interaction, the underlying physical models should be enhanced to obtain better simulation accuracy increasing the modeling complexity and relaxing the simulation timing constraints to properly solve the set of equations under analysis. However, in the specific field of haptic interaction, the desiderata are to have both physical consistency and high frame rate to display stable and coherent stimuli as feedback to the user requiring a tradeoff between accuracy and real-time interaction. This work introduces a haptic system for the evaluation of the fabric hand of specific garments either existing or yet to be produced in a virtual reality simulation. The modeling is based on the co-rotational Finite Element approach that allows for large displacements but the small deformation of the elements. The proposed system can be beneficial for the fabrics industry both in the design phase or in the presentation phase, where a virtual fabric portfolio can be shown to customers around the world. Results exhibit the feasibility of high-frequency real-time simulation for haptic interaction with virtual garments employing realistic mechanical properties of the fabric materials

Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment

Adhesion State Estimation for Electrostatic Gripper Based on Online Capacitance Measure

Electroadhesion is a suitable technology for developing grippers for applications where fragile, compliant or variable shape objects need to be grabbed and where a retention action is typically preferred to a compression force. This article presents a self-sensing technique for electroadhesive devices (EAD) based on the capacitance measure. Specifically, we demonstrate that measuring the variation of the capacitance between electrodes of an EAD during the adhesion can provide useful information to automatically detect the successful grip of an object and the possible loss of adhesion during manipulation. To this aim, a dedicated electronic circuit is developed that is able to measure capacitance variations while the high voltage required for the adhesion is activated. A test bench characterization is presented to evaluate the self-sensing of capacitance during different states: (1) the EAD is far away from the object to be grasped; (2) the EAD is in contact with the object, but the voltage is not active (i.e., no adhesion); and (3) the EAD is activated and attached to the object. Correlation between the applied voltage, object material and shape and capacitance is made. The self-sensing EAD is then demonstrated in a closed-loop robotic application that employs a robot manipulator arm to pick and place objects of different kinds

Tests of stellar model atmospheres by optical interferometry IV: VINCI interferometry and UVES spectroscopy of Menkar

We present K-band interferometric and optical spectroscopic observations of Menkar obtained with the instruments VINCI and UVES at Paranal Observatory. Spherically symmetric PHOENIX stellar model atmospheres are constrained by comparison to our interferometric and spectroscopic data, and high-precision fundamental parameters of Menkar are obtained. Our high-precision VLTI/VINCI observations in the first and second lobes of the visibility function directly probe the model-predicted strength of the limb darkening effect in the K-band and the stellar angular diameter. The high spectral resolution of UVES allows us to confront observed and model-predicted profiles of atomic lines and molecular bands. We show that our derived PHOENIX model atmosphere for Menkar is consistent with both the measured strength of the limb-darkening in the near-infrared K-band and the profiles of spectral bands around selected atomic lines and TiO bandheads. At the detailed level of our high spectral resolution, however, noticeable discrepancies between observed and synthetic spectra exist. We obtain a Rosseland angular diameter of Theta_Ross=12.20 mas pm 0.04 mas. Together with the Hipparcos parallax, it corresponds to R_Ross=89 pm 5 R_sun, and together with the bolometric flux to T_eff=3795 K pm 70 K.Our approach illustrates the power of combining interferometry and high-resolution spectroscopy to constrain and calibrate stellar model atmospheres. The simultaneous agreement of the model atmosphere with our interferometric and spectroscopic data increases confidence in the reliability of the modelling of this star, while discrepancies at the detailed level of the high resolution spectra can be used to further improve the underlying model.Comment: 11 pages, 5 figures, accepted by A&A. Also available from http://www.aanda.org/articles/aa/pdf/forth/aa6032_06.pd

Modeling multiple vehicle interaction constraints for behavior prediction of vehicles on highways

In the context of autonomous driving and road situation awareness, this manuscript introduces a Bayesian network that enables the prediction of participant vehicles (PVs) circulating on a highway. The network architecture combines Long-Short-Term-Memory recurrent Deep networks and Support Vector Machines as computational nodes in a graph. The network inputs are real data acquired by radar and synthetic data generated mimicking the real ones. The interaction among multiple vehicles is handled explicitly by introducing the Allowance Factors that model the constraints of the possible interactions of the PVs and the ego car in the trajectory forecast estimation. Results obtained on the conducted tests show the ability of the system to predict the motion of the vehicles up to 6 s in advance with a 92% mean average accuracy. Additionally, if implemented into automatic trajectory planners, the introduced modeling approach enables the prediction and avoidance of possible vehicles’ collisions