Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Robot greeting: tracking human greeting mental states and acting accordingly

Mobile social robots should be able to engage in interaction with people effectively. However, greeting someone is a complex task since it implies an exchange of social signals. Adam Kendon modeled human greetings as a set of six phases: initiation of approach, distance salutation, head dip, approach, final approach, and close salutation. Based on Kendon’s model, we propose a system for mobile social robots that manages the greeting process through the exchange of social signals. A Hidden Markov Model keeps track of the greeting stage through the observation of the human gestures, while a behavior tree generates appropriate robot actions. We used publicly available datasets to train the Hidden Markov Model. Evaluation on test sets showed an average greeting phase estimation accuracy of 80.9%. We tested the full system (Hidden Markov Model + Behavior Tree) in simulation and in a real world pilot experiment using the Vizzy robot, and it recognized and replicated the correct phase with an accuracy of 91.8% and 53.8%, respectively.
PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning

Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the objects are variable-sized and the configurations are complex. In this work, we present the PackerBot, a complete robotic pipeline for performing variable-sized product packing in unstructured scenes. First, by leveraging the imperfect experience of human packer, we propose a heuristic DRL framework for learning optimal online 3D bin packing policy. Then we integrate it with a 6-DoF suction-based picking module and a product size estimation module, leading to a complete product packing system, namely the PackerBot. Extensive experimental results show that our method achieves the state-of-the-art performance in both simulated and real-world tests. The video demonstration is available at: https://vsislab.github. io/packerbot.
Map-Aided Fusion of IMU PDR and RSSI Fingerprinting for Improved Indoor Positioning

At present, indoor localization becomes an attractive research area enabling many opportunities. Although there are several solutions for indoor localization, the standalone localization methods suffer from various limitations that affect the localization accuracy. This study presents a map matching-based lightweight sensor fusion technique that can combine the IMU-based PDR with the RSSI fingerprinting method to achieve high precision position estimates. Spatial knowledge from the indoor floor plan is used to implement a landmark-assisted PDR to bound the accumulation error. Moreover, the KD-tree searching method along with a set of map matching techniques are exploited to the proposed sensor fusion technique that reduces the fingerprint search space while eliminates the spatial ambiguity problem of the RSSI. The proposed method was evaluated and compared with several standalone techniques. Results demonstrated that the proposed fusion method yields a median positioning accuracy of 0.73 m and outperformed the considered standalone methods.
Telemanipulation via Virtual Reality Interfaces with Enhanced Environment Models

Extreme environments, such as search and rescue missions, defusing bombs, or exploring extraterrestrial planets, are unsafe environments for humans to be in. Robots enable humans to explore and interact in these environments through remote presence and teleoperation and virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. However, current virtual reality interfaces are still very limited in their capabilities. In this work, we aim to advance robot teleoperation virtual reality interfaces by developing an environment reconstruction methodology capable of recognizing objects in a robot’s environment and rendering high fidelity models inside a virtual reality headset. We compare our proposed environment reconstruction method against traditional point cloud streaming by having operators plan waypoint trajectories to accomplish a pick-and-place task. Overall, our results show that users find our environment reconstruction method more usable and less cognitive work compared to raw point cloud streaming.
SoMo: Fast and Accurate Simulations of Continuum Robots in Complex Environments

Engineers and scientists often rely on their intuition and experience when designing soft robotic systems. The development of performant controllers and motion plans for these systems commonly requires time-consuming iterations on hardware. We present the SoMo (Soft Motion) toolkit, a software framework that makes it easy to instantiate and control typical continuum manipulators in an accurate physics simulator. SoMo introduces a standardized and human-readable description format for continuum manipulators. It leverages this description format and the Bullet physics engine to enable fast and accurate simulations of soft and soft-rigid hybrid robots in environments with complex contact interactions. This allows users to vary design and control parameters across simulations with minimal effort. We compare the capabilities of SoMo to other physics simulators and highlight the benefits and accuracy of SoMo by demonstrating the agreement between simulation and real-world experiments on several examples; these include an in-hand manipulation task with continuum fingers, an automated exploration of how to design soft fingers for precision grasping, and a brief snake locomotion study. Overall, SoMo provides an accessible way for designers of soft robotic hardware and control systems to gain access to a simulation-accelerated workflow.
Manipulating a Whip in 3D via Dynamic Primitives*

A prominent challenge in the field of robotics is manipulation of flexible objects. One major factor that makes this task difficult is the complex dynamics emerging from its high-dimensional structure. This argues against the use of popular optimization-based approaches, which scale poorly with system dimension (the "curse of dimensionality"). Nevertheless, almost indifferent to this complexity, humans handle it on a daily basis, without any apparent difficulty.Inspired by human motor control, we propose that encoding movements based on dynamic primitives can simplify the task of manipulating flexible objects and provides a way around the curse of dimensionality. Using an extreme example — manipulating a whip — we tested in simulation whether targets at various locations could be reached with a whip by using a controller based on dynamic primitives. Regardless of the target location, this approach successfully managed the complexity of a 54 degree-of-freedom system (yielding a 108-dimensional state-space representation) and identified an upper-limb movement that achieved the task. This approach did not require a detailed model of the whip, which thereby significantly simplified the computational complexity of the control task. We believe that this approach may facilitate robotic manipulation of flexible materials, and in general afford a simplified way to control dynamically complex objects.
An Analysis on the Modeling Accuracy of Industrial Manipulators with Inherent Joint Elasticity

High precision industrial applications call for equally precise functioning of industrial manipulators, which in turn requires accurate modeling of the manipulators. This paper carries out a detailed study on the modeling of industrial manipulators with elastic joints to improve their accuracy. In particular, the effect of adopting a simple harmonic drive (HD) model and ignoring a dynamic effect called low inertia coupling between the actuators and links on the model accuracy has been analyzed from a parameter estimation perspective. Since the aforementioned model characteristics have been generally ignored for high gear reduction ratios, this study is carried out with five different reduction ratios ranging from low to high, where three different models of a three-joints elastic manipulator are considered. The accuracy of the models is compared using the torque performance metrics of a predefined joint motion of the robot. Furthermore, the impact of the models with different accuracy is assessed by carrying out a state-of-the-art dynamic parameter estimation, and the resulting errors are compared to ascertain the merits of adopting a detailed elastic dynamic model of a manipulator.
Remote Q factor measurement system for a ATS coil using digital phase locked loop

For safety reasons, the ATS (automatic train stop) system is used, which is a kind of communication system with a feedback amplifier and a transformer on the train and wayside coils. The coils are highly resonant LC circuits, and also have very high Q (quality) factors. The Q factors of wayside ATS coils have to be maintained high enough for the amplifier to operate reliably. In this paper, a novel Q measurement system is proposed. The system measures the resonant frequency and the bandwidth of the ATS coils, by controlling the phase difference between the transformer and the coil using a digital PLL (phase locked loop). The overall configuration and algorithms of the proposed system and the digital PLL control schemes are presented in detail. Experimental waveforms are shown to verify the system performance.
MDN-VO: Estimating Visual Odometry with Confidence

Visual Odometry (VO) is used in many applications including robotics and autonomous systems. However, traditional approaches based on feature matching are computationally expensive and do not directly address failure cases, instead relying on heuristic methods to detect failure. In this work, we propose a deep learning-based VO model to efficiently estimate 6-DoF poses, as well as a confidence model for these estimates. We utilise a CNN - RNN hybrid model to learn feature representations from image sequences. We then employ a Mixture Density Network (MDN) which estimates camera motion as a mixture of Gaussians, based on the extracted spatio-temporal representations. Our model uses pose labels as a source of supervision, but derives uncertainties in an unsupervised manner. We evaluate the proposed model on the KITTI and nuScenes datasets and report extensive quantitative and qualitative results to analyse the performance of both pose and uncertainty estimation. Our experiments show that the proposed model exceeds state-of-the-art performance in addition to detecting failure cases using the predicted pose uncertainty.
Carbon Emission Analysis of Electrical Machines

The industry places increased importance on carbon reduction. Many are focused on lowering the fuel consumption of electrical machines, however, carbon emissions from the acquisition of raw materials and the energy consumption of manufacturing processes also contribute significantly. Therefore, a carbon emission analysis model is particularly necessary for electrical machines. This paper presents an evaluation method for the carbon emissions of the production of industrial synchronous generator. The results concluded can be used to reduce the carbon emission of electrical machines, material waste and energy consumption, moreover, identify a detailed investigation of the impact between raw material acquisition, manufacturing process planning and carbon emissions.
Sensorless Control System of PMSM Based on Double Sliding Mode Active Disturbance Rejection Controller

Sliding mode controller (SMC) has good disturbance rejection ability. It can replace the PI controller in permanent magnet synchronous motor (PMSM) control system to enhance the performance. However, it exists the chattering problem, which may cause the system instable. An integrated position-velocity-current loop sliding mode active disturbance rejection controller (PSM-ADRC) is proposed to suppress the chattering. So as to solve the puzzle of reliability reduction resulted by position sensors, a new sliding mode observer (SMO) is utilized, whose reaching rate is improved by the segmentation power function. The simulation results show the superiority of the sensorless control system of PMSM based on double sliding mode active disturbance rejection controller.
Towards All-Polymeric Cochlear Implant Micro-Electrode Arrays

This paper shows that PEDOT:PSS is an excellent material for all-polymeric cochlear implant micro-electrode arrays. Initial experiments have shown a high conductivity of 230 S/cm for PEDOT:PSS samples, which dramatically decreased to 0.48 S/cm after 3 hours of UV treatment. Electrical characterisation of PEDOT:PSS electrodes reveals that its maximum charge injection capacity is 15 times higher than that of platinum, the electrode material used in commercial cochlear implants. These experiments demonstrate that PEDOT:PSS is an excellent candidate material for cochlear implants, both as micro-electrode and insulating layer.
Evolving Infotaxis for Meandering Environments

Locating odour sources with mobile robots is a difficult task with many real world applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. One of the most popular cognitive approaches is Infotaxis, which computes a probability map for the location of the chemical source and, on each time step, moves the robot in the direction that minimises the entropy of that probability map. The main difficulty for applying Infotaxis in the real world is selecting proper values for the parameters of its internal gas dispersion model, as it has been shown that its performance is greatly influenced by the accuracy of said model. This work proposes a Genetic Algorithm for optimising those parameters for specific environments. The proposed method is applied to environments with distinct wind and odour dispersion characteristics and the resulting parameters are compared. Moreover, the performance of Infotaxis is compared to that of reactive search strategies evolved by Geometric Syntactic Genetic Programming. The statistically validated results show that the evolved reactive strategies achieve equivalent success rates to Infotaxis, while being significantly faster. Real world experiments conducted in a controlled wind tunnel validated the simulation results.
Fiber-wireless sensor system based on a power-over-fiber technique

Abstract. A fiber-wireless sensor system based on a power-over-fiber technique is developed to offer a flexible, distributed sensing ability over a middle distance, especially under environments that are sensitive to electromagnetic interference. In this system, the optical energy of a high-power laser in the base station is transmitted via a fiber and then converted into electrical energy by a photovoltaic power converter (PPC) in the remote unit. This optically power-supplied remote unit operates as the coordinator in the wireless sensor network (WSN) and exchanges the sensing information with the base station via another fiber. In our demonstration system, the sensing information can be collected by a WSN 2 km away and be transmitted back. In order to improve the power supply ability of PPC, a maximum power point tracking technique is applied. More than 80% of PPC’s maximum output power can be obtained. Moreover, to reduce the power consumption of the remote unit and the sensor nodes, a simple and stable low-power communication protocol is designed.
A Study on the Effect of Energy Storage Systems and Distributed Generators on Reliability

Recently, as many researchers introduced various methods and factors for distributed renewable energy resources and energy storage systems to the grid, the need for verification on their performance and effect to the grid is emerging. So, in this paper, we predict system marginal price (SMP) by using long short-term memory (LSTM) based on empirical data published by the Korea Power Exchange and the Korea Meteorological Administration. After then, this paper suggests an optimal energy storage system (ESS) operation strategy based on the predicted data and the dynamic programming (DP) algorithm. To verify the impact of the proposed operation strategy on power grid, a grid fault is simulated through OpenDSS to consider the power system reliability. As a result, the proposed algorithm was able to interpret the effect on the system when the ESS operator shows selfish behaviors to the system.
Assessing Grasp Quality using Local Sensitivity Analysis

We propose a new approach to investigate and quantify dynamic grasp performance. Oftentimes, existing approaches to grasp analysis assess a grasp’s quality in a static situation. We build upon such considerations to also account for the dynamic nature of most grasp operations. In particular, these typically do not, in practice, occur in a static setting. Robotic grasping is indeed commonly involved in, for instance, pick-and-place operations which involve movement and thus a dynamic aspect. We investigate grasp quality over such movements, affording consideration not only to the gripper’s and grasp configuration, but also to their trajectory. More specifically, we explore the relationship from the gripper’s base acceleration to the stability of the grasped object (assessed using the relative acceleration of the object with respect to that of the gripper), using linear approximations of the corresponding dynamics. From such relations, we construct a grasp’s robustness metric, which accounts for the movements involved in the considered scenario. Numerical simulations are used to compare achieved results with those obtained using alternate existing methods. We illustrate merit of the proposed metric by exploring robustness of a given grasp under different trajectories.
Fast Autonomous Robotic Exploration Using the Underlying Graph Structure

In this work, we fully define the existing relationships between traditional optimality criteria and the connectivity of the underlying pose-graph in Active SLAM, characterizing, therefore, the connection between Graph Theory and the Theory Optimal Experimental Design. We validate the proposed relationships in 2D and 3D graph SLAM datasets, showing a remarkable relaxation of the computational load when using the graph structure. Furthermore, we present a novel Active SLAM framework which outperforms traditional methods by successfully leveraging the graphical facet of the problem so as to autonomously explore an unknown environment.
LiDAR-based Drivable Region Detection for Autonomous Driving

For autonomous driving, drivable region detection is one of the most basic and essential tasks. In this paper, a novel LiDAR-based drivable region detection algorithm which could output a complete, accurate and stable result is proposed. To promote the completeness of the detection result, the Bayesian generalized kernel inference and bilateral filtering are utilized to estimate the attribute of those unobserved cells. To ensure the traversability, a region growing operator is performed on the normal vector map which reflects the slope of the terrain, thus closely related to the traversability of the vehicle. To improve the result’s stability, information from multiple frames are fused together in the Kalman Filter framework. Experiments are performed both on public dataset and our own dataset. Experimental results show that the proposed algorithm could run in real-time and outperforms state-of-the-art approaches.
Robust Policy Search for an Agile Ground Vehicle Under Perception Uncertainty

Learning robust policies for robotic systems operating in presence of uncertainty is a challenging task. For safe navigation, in addition to the natural stochasticity of the environment and vehicle dynamics, the perception uncertainty associated with dynamic entities, e.g. pedestrians, must be accounted for during motion planning. To this end, we construct an algorithm with built-in robustness to uncertainty by directly minimizing an upper confidence bound on the expected cost of trajectories instead of employing a standard approach based on minimizing the expected cost itself. Perception uncertainty is incorporated into the policy search framework by predicting each pedestrian’s intent belief and propagating their state distribution in time using closed-loop goal-directed dynamics. We train the policy in simulation and show that it could be transferred to an agile ground vehicle for successful autonomous robot navigation in presence of pedestrians with perception uncertainty. We further show the superior performance of this policy over a policy that does not consider pedestrian intent and perception uncertainty.
Motor-Imagery EEGNet-Based Processing on a Low-Spec SoC Hardware

One of the most popular Brain-Computer Interface (BCI) paradigms is the classification of motor imagery tasks using Electroencephalograph signals (EEG). Recent works suggest the use of Convolutional Neural Networks (CNNs) to both extract the EEG features and classify them in a single compact solution. Since BCIs are meant to be run in embedded hardware, compact models and data reduction strategies are necessary. An EEGNet-based model is presented in this work, which achieves results similar to those of the state-of-the-art of 83.15 %, 75.74 % and 65.75 % in classification accuracy on 2-, 3-, and 4-class MI tasks in global validation on the Physionet Motor Movement/Imagery dataset. Taking advantage of its lower model complexity, a preliminary FPGA processor design using fixed-point datatypes is introduced, to evaluate resources consumption and latency on a low-spec system on chip approach.
Electron traps in bulk and epitaxial GaAs crystals

Fifteen different electron traps have been characterised in v.p.e. , l.p.e. , m.b. e. and bulk-grown GaAs from d.l. t.s. experiments. An accurate description of each level is given, and allows a fruitful comparison with fragmentary previous data. A catalogue of these electron traps is provided as a working tool.
Autoencoder-based Ultrasonic NDT of Adhesive Bonds

We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders.The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects.The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.
Automatic Feature Extraction and Traffic Management Using Machine Learning and Open CV Model

Artificial intelligence covers a vast area of the real time domain which supports humans for all activities. Machine learning (ML) techniques learn the data and react based on the properties of these data. The properties are identified by extracting the features from the extracted data. Image and video processing methods are essentials in real time application due the IoT (Internet of Things) devices. The data of these types of data is more complex and also high dimensional in nature. These dimensions are reduced by performing reduction techniques before performing the classification process. The proposed ML model targets the traffic management by automating the traffic light based on the flow in the road. The traffic priority is assigned based on the congestion level on the road. The traffic classification is done by considering different features and infrastructure maintained by the city. Existing system suffers the problem due to the following reasons such as traffic congestion, longer waiting time, improper maintenance of the traffic signal, and high carbon emission and so on. The objective of the proposed model is to reduce the traffic congestion by performing traffic flow conditions and make the people comfortable level during the travel.