Miracle-dz/UniFAR-FTUs huggingface dataset:
Passivity Based Iterative Learning of Admittance-Coupled Dynamic Movement Primitives for Interaction with Changing Environments

Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact wrench feedback in the adaptation and learning process of the DMP. Despite first approaches exist in this direction, no fully integrated approach has been proposed so far. In this paper, we introduce a new class of admittance-coupled DMPs that addresses environmental changes by including contact wrench feedback dynamics into the DMP formalism. Moreover, a novel iterative learning approach is devised that is based on monitoring the overall system passivity analysis in terms of reference power tracking. Simulations and experimental results with the Kuka LWR robot maintaining a non-rigid contact with the environment (wiping a surface) are shown for supporting the validity of our approach.
Voltage-Driven Near Field Beam Shifting in an InP Photonic Integrated Circuit

A voltage-driven, continuously-tuned beam shifting device is proposed, simulated, and demonstrated in an InP photonic integrated circuit. Electrodes are applied on the left and right side of a mode-expanded waveguide to electronically control the refractive index profile and shift the optical beam by means of reverse bias voltage. The feasibility of a beam shifting of <inline-formula> <tex-math notation="LaTeX">$4.9~\mu \text{m}$ </tex-math></inline-formula> with a power consumption of the order of 4.5 pW and voltage of 10 V is numerically predicted for a beam shifting element with ideal electrical isolation and fundamental input mode excitation. The reduction in beam-shifting efficiency is subsequently quantified for imperfect electrical isolation and in the presence of higher order mode excitation at the input to the beam shifting element. An experimental proof of concept is implemented by realizing the device on a generic photonic integration platform and co-integrating an on-chip tunable laser. High-resolution near-field measurements show near-field beam shifting of <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{m}$ </tex-math></inline-formula> with an applied voltage range from −4 to 0 V.
Authentication of Aerial Input Numerals by Leap Motion and CNN

As information technology has advanced in recent years, services which include personal authentication systems such as ATM are increasing. Current main personal authentication systems include IC cards, passwords, and biometrics authentication such as fingerprint authentication. However, there are several problems in these systems. Therefore, better systems are needed.As such systems, we propose a method to write numerals in the air using the Leap motion and to carry out personal authentication from such aerial handwriting data. We try to authenticate numerals 0 to 9 which are written by three subjects. After applying some pre-processing to inputs, learning and identification are carried out using CNN which is a method of machine learning. As a result, average identification accuracy was 90.3%. From this result, it is suggested that input numerals in the air can be authenticated and there is a possibility to construct a new personal authentication system.
Outage Probability of Vital Signs Detecting Radar Sensor System

Monitoring vital signs using microwave signals has significant applications in biology, and also for finding survivors under debris during natural hazards. The vital signs detection radar sensor system consists of a transmitter and a receiver. This system can be considered as a wireless system. In the wireless system, the outage probability is a crucial factor to evaluate the reliability of the system. This work investigates the outage probability (OP) of a radar sensor system under the Nakagami-m environment. The dependence of the OP on different parameters like operating frequency, distance to the measured object, transmitter power, and the leakage between transmitter and receiver antennae is examined. The theoretical model for the OP is validated with simulation results through various environments.
Enhancing the Per-Unit-Length Data Density in Near-Field Chipless-RFID Systems With Sequential Bit Reading

In this letter, a novel near-field chipless-Radio Frequency Identification (RFID) system with sequential bit reading is reported. The main novelty concerns the tags, implemented by means of straight half-wavelength resonators separated by a small distance and forming a linear chain in the direction orthogonal to their axis. By this means, the number of resonant elements (or bits) per unit length of the tag is significantly increased, as compared to the previous implementations, with the result of very competitive tags in terms of data density. The active part of the reader is a microstrip line loaded with a shunt stub. Through a proper design, it is possible to achieve a significant excursion of the transmission coefficient when the stub is either loaded or unloaded with a functional resonant element of the tag. Consequently, by tag motion over the reader (stub), it is possible to infer the Identification (ID) code by simply injecting a harmonic signal to the input port of the stub-loaded line (the ID code is contained in the amplitude modulated, signal at the output port). This system has been validated by considering 100 b tags exhibiting a surface data density of 4.9 b/cm2. However, the relevant parameter is the data density per unit length, as high as 16.7 b/cm, i.e., much larger than previous values reported in the literature in similar chipless-RFID systems.
Fast Trajectory Planning for Automated Vehicles Using Gradient-Based Nonlinear Model Predictive Control

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.
A Sensor Fusion Approach to Indoor Human Localization Based on Environmental and Wearable Sensors

The goal of this paper is to localize a resident in indoor environments by using motion information from distributed environmental sensors and body activity information from wearable sensors. The passive infrared sensor nodes distributed in a home provide binary information about human motion in their field of views, while the wearable inertial measurement unit sensor node collects motion data that can be used in body activity recognition, walking velocity, and heading estimation. Basic human activities such as sitting, sleeping, standing, and walking are recognized. We proposed a particle filter-based sensor fusion algorithm that takes advantage of the human location/activity correlation in indoor environments to increase the localization accuracy. Experiments were conducted in a mock apartment testbed. We used the ground truth data obtained from a motion capture system to evaluate the results. Note to Practitioners—This paper is motivated by a goal to realize various home automation applications through human localization. For example, with the knowledge of human location, lighting, and air conditioning systems can be more efficiently managed, or a home service robot can provide better service to the resident. There currently lacks an accurate human localization method in indoor environments. The theoretical framework proposed in this paper aims to combine simple environmental sensors (passive infrared sensors) and a single wearable motion sensor to accurately track human locations. This method reduces the intrusiveness to the human, while keeping the cost of the infrastructure low. The theoretical framework has been tested and evaluated in a mock apartment environment as a proof of concept. For deployment into real homes, we need to conduct more realistic tests and thorough evaluation of the system. It is also necessary to reduce the size and weight of the wearable motion sensor and simplify the installation of the environmental sensors.
Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects

Reinforcement learning for assembly tasks can yield powerful robot control algorithms for applications that are challenging or even impossible for “conventional” feedback control methods. Insertion of a rigid peg into a deformable hole of smaller diameter is such a task. In this contribution we solve this task with Deep Reinforcement Learning. Force-torque measurements from a robot arm wrist sensor are thereby incorporated two-fold; they are integrated into the policy learning process and they are exploited in an admittance controller that is coupled to the neural network. This enables robot learning of contact-rich assembly tasks without explicit joint torque control or passive mechanical compliance. We demonstrate our approach in experiments with an industrial robot.
Indoor Mapping and Localization for Pedestrians using Opportunistic Sensing with Smartphones

Indoor localization for pedestrians has gained increasing popularity among the rich body of literature for the last decade. In this paper, a low-cost indoor mapping and localization solution is proposed using the opportunistic signals from ambient indoor environments with a smartphone. It is composed of GraphSLAM-based offline mapping and Bayesian filtering-based online localization using generated signal maps. The GraphSLAM front-end is constructed by motion constraints from pedestrian dead-reckoning (PDR), loop-closure constraints identified by magnetic sequence matching with WiFi signal similarity validation, and observation constraints from opportunistic magnetic headings after error rejection. Globally consistent trajectories are created by graph optimization, after which signal maps (e.g., WiFi, magnetic fields, lights) are generated by Gaussian Processes Regression (GPR) for later localization. We propose to use the pseudo-wall constraints from the GPR variance map of magnetic fields and the lights measurements as observations for particle filtering. The proposed method is evaluated on several datasets collected from both the in-compass office buildings and outside public areas. Real-time localization is demonstrated on a smartphone in an office building covering 2000 square meters with the 50- and 90-percentile accuracies being 2.30 m and 3.41 m, respectively.
Dynamic balance control based on an adaptive gain-scheduled backstepping scheme for power-line inspection robots

This paper presents an adaptive gain-scheduled backstepping control ( AGSBC ) scheme for the balance control of an underactuated mechanical power-line inspection ( PLI ) robotic system with two degrees of freedom and a single control input. First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points, an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control ( GSBC ) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.
Range-Based Collaborative MSCKF Localization

In this paper, a framework for collaborative localization of heterogenous systems is presented. Making advantage of the original MSCKF framework, we design a collaborative MSCKF filter that operates in two levels and allows a decentralized 3D collaborative localization without use of external computation systems. To achieve that, based on MSCKF localization, we first propose a range based collaboration that we optimize using the extracted environment constraints, an operation allowed by the use of a truncated unscented kalman filtering updates. The collaborative filtering is managed to not impact the original MSCKF odometry properties. The framework is applied to collaborative localization of aerial and ground robots; experimental results show the effectiveness of the proposed method.
Current-Based Gear Fault Detection for Locomotive Gearboxes

Our objective is to analyze the traction motor stator current in the diagnosis of the gearbox fault of the locomotive. The gear localized defects cause torque oscillations which modulate the frequency components in the stator current signal and generate the sidebands in the stator current power spectrum. However, the sideband components are weak and covered by noise. Dual-tree complex wavelet transform is used to extract fault features hidden in the sidebands. We use wavelet energy entropy to make a preliminary diagnosis of the signal, and then apply a dual-tree complex wavelet transform in the current signal to further determine the type of failure. In order to illustrate the validity of the proposed diagnosis method, simulation signal analysis was carried out and then verified in the laboratory. Finally, gear diagnosis experiments on the freight locomotive show that the proposed method can effectively diagnose gear failures.
Human Intention Estimation based on Neural Networks for Enhanced Collaboration with Robots

In human-robot collaboration, the robot is required to provide assistance to the user by facilitating task execution. However, due to stability requirements, a well-damped admittance behavior of the robot is necessary during interaction, thus inducing fatigue in the operator. While available schemes involve variable impedance controllers to mitigate this effect, here we propose an alternative approach entailing a proactive robot behavior that assists in the cooperative execution of trajectories towards desired goals, by estimating the user intention. To this end, we make use of Recurrent Neural Networks (RNNs) to predict and classify cooperative motions, on the basis of a set of predefined goals in the workspace and model-based generated data of human movements. Manual guidance validation experiments are conducted on a 6 d.o. f. ABB IRB140 industrial robot equipped with a force sensor.
Lightweight and Low-Loss 3-D Printed Millimeter-Wave Bandpass Filter Based on Gap-Waveguide

This paper presents a comprehensive study of a groove gap waveguide (also known as a waffle-iron) bandpass filter at Ka-band (26.5-40 GHz), fabricated using a high resolution polymer jetting (Polyjet) 3-D printing technology. The same filter was previously fabricated using brass CNC milling technology. The metalized Polyjet 3-D printed filter has lower loss, is lighter in weight and more cost-effective, when compared to the solid metal case. The filter operates at a center frequency of 35.65 GHz, has a 500 MHz bandwidth (1.4% fractional bandwidth), and has a transmission zero below and above the passband. Without any design iterations, the measured S-parameters for the Polyjet 3-D printed filter are presented and compared with simulated results, showing excellent agreement. A comparison is then made between the measured results and that of its brass machined counterpart. The new Polyjet 3-D printed filter is 85% lighter than the conventional machined version. All these features prove the important potential of 3-D printing technology for millimeter-wave applications, which includes aerospace.
Audio Feature Extraction Based on Sub-Band Signal Correlations for Music Genre Classification

We present novel low-level audio features that are based on correlations between sub-band audio signals decomposed by undecimated wavelet transform. Under the assumption that SVM is used for classifier learning, the experimental results on GTZAN dataset showed that the proposed method demonstrated the best accuracy of 81.5%, outperforming the conventional methods.
Time Synchronization in Wireless Sensor Networks: A Modeling and Analysis Experience Using Theatre

This paper describes a modelling and analysis experience concerning time synchronization in wireless sensor networks (WSN). A fully distributed algorithm is formally modelled and its properties assessed through statistical model checking. The described work is based on the Theatre framework which rests on actors and asynchronous message passing. Theatre can be reduced to the Uppaal Statistical Model Checker (SMC). The paper discusses the chosen time synchronization algorithm, outlines the Theatre modelling features and its mapping on to Uppaal Smc, and shows a Theatre model for the selected time synchronization algorithm enhanced with a new adaptation mechanism for energy saving. The model is then analyzed through simulations.
Deep Q-Learning for Dry Stacking Irregular Objects

We propose a reinforcement learning approach for automatically building dry stacked (i.e. no mortar) structures with irregular objects. Stacking irregular objects is a challenging problem since each assembly action can be drawn from a continuous space of poses for an object, and several local geometric and physical considerations strongly affect the stability. To tackle this challenge, we concentrate on a simplified 2D version of the problem. We present a reinforcement learning algorithm based on deep $Q$-learning, where the learned $Q$-function, which maps state-action pairs into expected long-term rewards, is represented by a deep neural network. As the action space is continuous the $Q$-network is trained by sampling a finite number of actions that consider both geometric and physical constraints to approximate the target $Q$-values, Experiments show that the proposed method outperforms previous heuristics-based planning, leading to super construction with objects containing a significant amount of variations. We validate the generated stacking plans by executing them using a robot arm and manufactured, irregular objects.
Key-Frame Strategy During Fast Image-Scale Changes and Zero Motion in VIO Without Persistent Features

Many of today's Visual-Inertial Odometry (VIO)frameworks work well under regular motion but have issues and need special treatment under special motion. Here, special does not imply bad or corrupted data but stands for increased difficulty to treat clean data. Common special motion for VIO are large feature displacement due to fast motion close to a scene and zero motion phases not providing sufficient baseline. In this paper we present a feature and frame selection approach which seamlessly handles all motion scenarios without the need of (error prone)motion case identification and subsequent case-specific heuristics. We further show that this approach allows to eliminate features in the state vector (persistent features)altogether while still being able to inherently handle zero motion phases. This reduces computational complexity while maintaining the ability to hover in place. We integrate our frame selection approach into our own VIO algorithm and compare its performance against three state-of-the-art algorithms with real data on a real platform. While our approach shows slightly higher global drift it is the only algorithm that can reliably estimate the pose over a large motion spectrum from fast scale change down to zero motion.
Quantitative Schlieren Diagnostic Applied to a Nitrogen Thermal Plasma Jet

A quantitative interpretation of the schlieren technique applied to an atmospheric pressure, vortex-stabilized nitrogen thermal plasma jet generated in a direct-current nontransferred arc plasma torch (nitrogen gas flow rate of 25 NL/min, power level of 15 kW), discharging into ambient air is reported. A $Z$ -type, two-mirror schlieren system was used in the research. The technique allowed inferring the temporally averaged values of the temperatures and densities of different species present in the plasma jet in a wide range of radial and axial distances. Deviations from kinetic equilibrium in the calculation of the plasma refractive index were accounted for, but maintaining the assumption of the local chemistry equilibrium. The influence of several assumptions on the accuracy of the measurements was considered. The results have shown that for a distance of 3.5-mm downstream from the nozzle exit, the kinetic equilibrium is realized (being both electron and gas temperatures values around 11 000 K), but noticeable deviation from kinetic equilibrium appears toward the jet border. On the other hand, a marked deviation from the kinetic equilibrium was found in the whole far field of the plasma jet, where the electron temperature remains still quite high (about 10 000 K at 30-mm downstream of the nozzle exit), well decoupled from the gas temperature (about 7000 K at the same distance). The obtained results are in reasonable good agreement with those previously reported by some of the authors by using a double floating probe method in the same plasma torch.
Temperature Anomaly Location and Cable Equipment recognition with an Improved Convolutional Neural Network Method and Transfer Learning

With the improvement of industrialization, people’s requirements for power supply quality also increase year by year. Therefore, how to ensure the normal supply of electricity has become a very concerned issue for power maintenance personnel. In this paper, we use the image captured by the cable tunnel inspection system to locate the cable connector in the image and map it to the infrared image based on the convolution neural network method. Based on the Analysis of the temperature of the cable connector in time, the monitoring system is able to make an alarm on the abnormal situation which can maintain the safety of power supply and extend the service life of the cable. Aiming at the small number of sample images collected, a transfer learning method is adopted, which reduces the training intensity and ensures a better positioning and recognition effect.