Miracle-dz/UniFAR-FTUs huggingface dataset:
A Universal Controller for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have become popular in a wide range of applications, including many military and civilian uses. State of the art control strategies for these vehicles are typically limited to a portion of the vehicle's flight envelope, and are tailored to a specific type of platform. This article presents a single physics-based controller capable of aggressive maneuvering for the majority of UAVs. The controller is applicable to UAVs with the ability to apply a force along a body-fixed direction, and a moment about an arbitrary axis, which includes UAVs such as multi-copters, conventional fixed-wing, agile fixed-wing, flying-wing with two thrusters, most tailsitters, and some tilt-rotor/wing platforms. We demonstrate autonomous flight for a quadrotor and agile fixed-wing aircraft in a simulation environment. To specifically demonstrate the extreme maneuvering capability of the control logic, we perform a rolling flip with the quadrotor and an aggressive turnaround with the fixed-wing aircraft, all using a single controller with a single set of gains.
DLWV2: A Deep Learning-Based Wearable Vision-System with Vibrotactile-Feedback for Visually Impaired People to Reach Objects

We develop a Deep Learning-based Wearable Vision-system with Vibrotactile-feedback (DLWV2)to guide Blind and Visually Impaired (BVI)people to reach objects. The system achieves high accuracy in object detection and tracking in 3-D using an extended deep learning-based 2.5-D detector and a 3-D object tracker with the ability to track 3-D object locations even outside the camera field-of-view. We train our detector with a large number of images with 2.5-D object ground-truth (i.e., 2-D object bounding boxes and distance from the camera to objects). A novel combination of HTC Vive Tracker with our system enables us to automatically obtain the ground-truth labels for training while requiring very little human effort to set up the system. Moreover, our system processes frames in real-time through a client-server computing platform such that BVI people can receive realtime vibrotactile guidance. We conduct a thorough user study on 12 BVI people in new environments with object instances which are unseen during training. Our system outperforms the non-assistive guiding strategy with statistic significance in both time and the number of contacting irrelevant objects. Finally, the interview with BVI users confirms that our system with distance-based vibrotactile feedback is mostly preferred, especially for objects requiring gentle manipulation such as a bottle with water inside.
Persistent Anytime Learning of Objects from Unseen Classes

We present a fast and very effective method for object classification that is particularly suited for robotic applications such as grasping and semantic mapping. Our approach is based on a Random Forest classifier that can be trained incrementally. This has the major benefit that semantic information from new data samples can be incorporated without retraining the entire model. Even if new samples from a previously unseen class are presented, our method is able to perform efficient updates and learn a sustainable representation for this new class. Further features of our method include a very fast and memory-efficient implementation, as well as the ability to interrupt the learning process at any time without a significant performance degradation. Experiments on benchmark data for robotic applications show the clear benefits of our incremental approach and its competitiveness with standard offline methods in terms of classification accuracy.
Pose Estimation for Mobile Robots to Maximise Data Quality of Fixed-Focus Laser Diagnostics in Hazardous Environments

Characterisation of nuclear environments is critical for long term operation and decommissioning. Laser Induced Breakdown Spectroscopy (LIBS) is an example of a scientific instrument that could be deployed to aid in characterisation of unknown environments. LIBS consists of a high intensity pulsed laser being focussed down onto a target to create a plasma, and optical emission from the plasma is then used to determine elemental composition of unknown materials. For robots deployed with these instruments in extreme environments, mission time can be limited by hazards present such as radiation. Once deployed a robot must be able to collect the best data possible whilst maximising operational runtime. We present a data quality based probabilistic approach to robot pose estimation to maximise data quality, by considering optimum sensor placement whilst avoiding harmful environmental features such as radiation for a fixed-focus laser diagnostic such as LIBS. This approach is able to determine optimum robot poses for arbitrary targets in 3D for arbitrary diagnostic mounting with respect to the robot. The approach is able to avoid obstacles and avoid occlusion of the target by said obstacles. This can be used as part of autonomous investigation and characterisation performed by mobile robots in hazardous environments.
Design of Compliant Mechanosensory Composite (CMC) and its Application Toward the Sensible Mesoscale Robotics

Sensed information greatly helps a robot to adjust its motion or modulate the locomotory behavior. While many sensing components have been developed for macroscale robots, such off-the-shelf sensors are hardly integrated with a mesoscale (i.e., 0.1 mm to 10 mm) robot due to the size limitation. In this work, we propose a Compliant Mechanosensory Composite (CMC) to fabricate a small compliant mechanism with embedded sensing ability. As the first demonstration of CMC, we directly print a conductive polymer PEDOT:PSS onto the flexible joint of a compliant mechanism to sense the motion of the flexible joint itself. Owing to the variation of electric contact resistance (ECR) upon bending, the CMC could estimate its bending angle. The performance of the CMC was verified by analyzing the cyclic bending, transient and stationary response. Overall, a sparsely printed serpentine pattern with thicker line exhibited consistent response without a noticeable hysteresis. To demonstrate the applicability of the CMC process, a small gripper actuated by a SMA (shape memory alloy) coil was fabricated, and its motion was successfully measured using the embedded sensors. We expect the proposed CMC will enable a small robot to become sensible at its self motion, external load, and physical contacts in future.
“Oh! I am so sorry!”: Understanding User Physiological Variation while Spoiling a Game Task

This paper investigates how individuals react in a situation when an experimenter (human or robot) either tells them to stop in the middle of playing the Jenga game, or accidentally bumps into a table and makes the tower fall down. The mood of the participants and different physiological parameters (i.e., galvanic skin response (GSR) and facial temperature variation) are extracted and analysed based on the condition, experimenter, and psychological questionnaires (i.e., TEQ, TEIQ, RST-PQ). This study was a between participants study with 23 participants. Our results show that multiple GSR parameters (e.g., latency, amplitude, number of peaks) differ significantly based on the condition and the experimenter the participants interacted with. The temperature variation in three regions of interest (i.e., forehead, left, and right periorbital regions) are good indicators of how ready an individual is to react in an unforeseen situation.
Investigation on Dynamic Stability of an Islanded Microgrid and Optimization for Its Controller Parameters

In this paper, a framework for dynamic stability analyses of a typical inverter-based islanded microgrid is presented. The studied microgrid consists of energy storage systems, which are controlled by two kinds of strategies, namely active/reactive power (P/Q) control and voltage/frequency (V/f) control, and photovoltaic (PV) generation, which is assumed to work in the maximum power point tracking (MPPT) mode, and constant active/reactive load. The comprehensive dynamic model of the considered microgrid is developed, based on which a bunch of small-signal models are deduced at stable operating points. Then by means of eigenvalue-theorem-based stability analysis, the relation between damping modes and state variables can be derived. Afterward, a global optimal method is implemented to improve system stability, which is based on the investigation of the controller parameters that affecting the traces of eigenvalues. The simulation results show that the improved system is more stable after suffering small disturbance of a light intensity step down and a step up.
Sentiment Analysis on the Level of Customer Satisfaction to Data Cellular Services Using the Naive Bayes Classifier Algorithm

Internet users in Indonesia have increased in recent years. Many product service providers who provide internet access services in accordance with tariff options and their superiority. In this research, sentiment analysis on social media to some service data service operator to see the level of public satisfaction in using data service of telecommunication operator for internet access in Indonesia.In this research is sentiment analysis with several stages, namely the collection of sentiment data using API (Application Programming Interface) which is available on Twitter. The preprocessing stage is then processed to process raw initial data, then perform POS tagging and weighing the word with TF-IDF calculation and perform classification using the Naive Bayes Classifier (NBC) method. This study yields an average value of 94,5% precision rate, 93,3% Recall and 99,09% Accuracy.
3D Deep Object Recognition and Semantic Understanding for Visually-Guided Robotic Service

For the success of visually-guided robotic errand service, it is critical to ensure dependability under various ill-conditioned visual environments. To this end, we have developed Adaptive Bayesian Recognition Framework in which in-situ selection of multiple sets of optimal features or evidences as well as proactive collection of sufficient evidences are proposed to implement the principle of dependability. The framework has shown excellent performance with a limited number of objects in a scene. However, there arises a need to extend the framework for handling a larger number of objects without performance degradation, while avoiding difficulty in feature engineering. To this end, a novel deep learning architecture, referred to here as FER-CNN, is introduced and integrated into the Adaptive Bayesian Recognition Framework. FER-CNN has capability of not only extracting but also reconstructing a hierarchy of features with the layer-wise independent feedback connections that can be trained. Reconstructed features representing parts of 3D objects then allow them to be semantically linked to ontology for exploring object categories and properties. Experiments are conducted in a home environment with real 3D daily-life objects as well as with the standard ModelNet dataset. In particular, it is shown that FER-CNN allows the number of objects and their categories to be extended by 10 and 5 times, respectively, while registering the recognition rate for ModelNet10 and ModelNet40 by 97% and 89.5%, respectively.
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data

This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained AlexNet on these images. Then, inertial data is converted into Signal Images (SI) and another convolutional neural network (CNN) is trained on these images. Finally, learned features are extracted from both CNN, fused together to make a shared feature layer, and these features are fed to the classifier. We experiment with two classifiers, namely Support Vector Machines (SVM) and softmax classifier and compare their performances. The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities. Experimental results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves state of the art results when compared to other recently proposed visual-inertial action recognition methods
Joint Model Feature Regression and Topic Learning for Global Citation Recommendation

Citation recommendation has gained increasing attention in recent years. In practice, researchers usually prefer to cite the most topic-relevant articles. Nevertheless, how to model the implicit correlations between topics and citations is still a challenging task. In this paper, we propose a novel citation recommendation model, called TopicCite, which mines such fine-grained correlations. We extract various citation features from citation network, and integrate the learning process of feature regression with topic modeling. At the recommendation stage, we expand the folding-in process by adding the topic influence of papers that correlated with user-provided information. TopicCite can also be considered a technique for extracting topic-related citation features from manually defined citation features, which can essentially improve the granularity of pre-extracted features. In addition, the unsupervised topic model is supervised and mutually reinforced by abundant citation features in TopicCite; thus, the proposed model can also extract more reliable topic distributions from citation data, which brings a new perspective to topic discovery on linked data. The experimental results on the AAN and DBLP datasets demonstrate that our model is competitive with the state-of-the-art methods.
Coordinated Frequency Control Strategy of BESS integrating high proportion of Wind power plant with a Steam turbine

Many inverters nowadays have the ability to control the output power from the renewables sources. They all are mainly PI-based controllers and have been proved to provide stable and controllable power to the output. These inverters are known as Synthetic inverters because they provide synthetic inertia to the power system. The main problem arising from implementing the Synthetic inverters is that the renewables cannot always work on MPPT, some power must be reserved that can provide inertia to the system. In many ways, this residual power is wasted during the low demand, which means that cost of electricity would be increased. A new research has been carried out on BESS to provide the synthetic inertia. BESS can emulate the internal response just like conventional power plants (CPP) as well as synthetic inverters. In this paper BESS providing PFR (Primary frequency Response), IR (Inertial Response), and DR (Damping Response) is implemented and its effect on wind and steam turbines are analyzed with simulations.
A Fail-Safe Semi-Centralized Impedance Controller: Validation on a Parallel Kinematics Ankle

This paper proposes the implementation of an impedance controller on the ankle level of COMAN+, a robot with parallel kinematics ankles actuated by a dual four-bar mechanism. The main contribution of the work is a realization of said control scheme that grants a less abrupt and safer robot response in case of system failures, that would cause the local joint torque controllers to lose their torque reference inputs. In particular, we propose a semi-centralized impedance control implementation which eliminates the instability of the pure joint torque control schemes used in the classical fully centralized methods when torque reference interruptions occur. Finally, we present experimental results, proving the effectiveness of our method and demonstrating how it ensures a safer behaviour compared to a fully centralized impedance control implementation when the communication to the ankle joints is interrupted. This paper is a follow-up work of [1], which presented and analyzed the parallel kinematics ankles.
Calculation of AC resistance for Contact Terminal Between the Ground Wire and Armor Rod Based on Axial Thermal Circuit Model

It is of great significance to acquire the AC resistance of ground wire and armor rod contact surface accurately when analyzing the breakage mechanism of the contact surface of ground wire. This paper presents a method for calculating the AC resistance of ground wire and armor rod contact surface based on axial thermal circuit. The thermal parameters in the axial thermal circuit model are determined by genetic algorithm. Moreover, the effects of different loading current and the time calculation step on the calculation results are analyzed. The study results show that the proposed method can effectively calculate the AC resistance of the contact surface at different temperatures. And the resistance at the steady state temperature decreases with the increase of loading current. The time calculation step has little effect on the calculation results.
Modeling and Control of Multiple Aerial-Ground Manipulator System (MAGMaS) with Load Flexibility

The MAGMaS (Multiple Aerial-Ground Manipulator System) was proposed in [1] as a heterogeneous system composed of multiple ground (mobile) manipulators and aerial robots to collaboratively manipulate a long/large-sized object and demonstrated therein for rigid load manipulation. Here, we extend this result of [1] to the case of load manipulation with flexibility, which is crucial for long/slender object manipulation, yet, not considered in [1]. We first provide a rigorous modeling of the load flexibility and its effects on the MAGMaS dynamics. We then propose a novel collaborative control framework for flexible load-tip pose tracking, where the ground manipulator provides slower nominal pose tracking with overall load weight holding, whereas the aerial robot allows for faster vibration suppression with some load weight sharing. We also discuss the issue of controllability stemming from that the aerial robot provides less number of actuation than the modes of the load flexibility; and elucidate some peculiar conditions for this vibration suppression controllability. Simulations are also performed to demonstrate the effectiveness of the proposed theory.
Reinforcement Learning with Symbolic Input-Output Models

It is well known that reinforcement learning (RL) can benefit from the use of a dynamic prediction model which is learned on data samples collected online from the process to be controlled. Most RL algorithms are formulated in the state-space domain and use state-space models. However, learning state-space models is difficult, mainly because in the vast majority of problems the full state cannot be measured on the system or reconstructed from the measurements. To circumvent this limitation, we propose to use input-output models of the NARX (nonlinear autoregressive with exogenous input) type. Symbolic regression is employed to construct parsimonious models and the corresponding value functions. Thanks to this approach, we can learn accurate models and compute optimal policies even from small amounts of training data. We demonstrate the approach on two simulated examples, a hopping robot and a 1-DOF robot arm, and on a real inverted pendulum system. Results show that our proposed method can reliably determine a good control policy based on a symbolic input-output process model and value function.
Read Channel Modeling and Neural Network Block Predictor for TDMR

Two-dimensional magnetic recording (TDMR) is a promising technique to achieve very high areal density up to 10 Tb/in2. Write/Read channel based on Voronoi model has been widely used to capture the effects of inter-track interference (ITI), inter-symbol interference (ISI) and irregular grains. Higher density relies on complicated 2-D signal processing algorithms. In this paper we improve the read process in terms of the reliability for read response. We develop a Neural Network Block Predictor (NNBP) to detect the recording data, then we investigate the most possible harmful transitions and patterns in TDMR.
A Wideband On-Chip Directional Coupler Using a Negative Capacitance Circuit

We propose a wideband lumped element directional coupler using lossy negative capacitance (NCAP). NCAP provides a broadband resonance effect combined with passive inductors and capacitors in a circuit, enabling the coupler to exhibit wideband directivity performance. In addition, a 3-dB loss in the NCAP enhanced the bandwidth at the expense of the transmission gain, which decreased by only 0.3 dB. The proposed coupler was implemented in the IBM 7RF 180-nm CMOS technology. The size of the core chip is $1180\,\,\mu \text {m} \times 570\,\,\mu \text{m}$ . The designed NCAP provides a capacitance of −13 pF using a supply voltage of 2.7 V and a current of 9.6 mA. The coupling was designed to be −10 dB at the center frequency of 1 GHz. The bandwidth was defined by the frequency range, which exhibited a directivity of more than 30 dB. The S-parameter measurements showed an absolute bandwidth ranging from 0.86 to 1.12 GHz, and a fractional bandwidth of 26%. In the bandwidth, the coupling had a 0.5-dB variation, the transmission loss was smaller than 2.14 dB, and the noise figures were 1.8–2.5 dB.
Optimized Contrast Enhancements to Improve Robustness of Visual Tracking in a SLAM Relocalisation Context

Robustness of indirect SLAM techniques to light changing conditions remains a central issue in the robotics community. With the change in the illumination of a scene, feature points are either not extracted properly due to low contrasts, or not matched due to large differences in descriptors. In this paper, we propose a multi-layered image representation (MLI) in which each layer holds a contrast enhanced version of the current image in the tracking process in order to improve detection and matching. We show how Mutual Information can be used to compute dynamic contrast enhancements on each layer. We demonstrate how this approach dramatically improves the robustness in dynamic light changing conditions on both synthetic and real environments compared to default ORB-SLAM. This work focalises on the specific case of SLAM relocalisation in which a first pass on a reference video constructs a map, and a second pass with a light changed condition relocalizes the camera in the map.
Optimal Constrained Trajectory Generation for Quadrotors Through Smoothing Splines

In this paper, we present a trajectory generation method for quadrotors based on the optimal smoothing B-spline. Compared to existing methods which rely on polynomial splines or time optimal control techniques, our method systematically addresses the issue of axes-coupled and interval-wise constraints. These constraints can be used to construct safe flying zones and satisfy vehicle's physical limits. The proposed approach has also been extended to generate trajectories from the nominal plan which consists of not only points but also lines and planes, opening a door for new improvements and applications. Moreover, a closed-form solution can be obtained for cases without inequality constraints. Such a solution is numerically stable for the large-scale fitting problem, which allows us to directly fit the human sketching input from the touch device and capture all subtle details. Our approach is verified by various real flight experiments.