Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Phase Hybrid Switched-Capacitor Interleaved AC-DC Boost Converter

In this paper a single-phase boost PFC multilevel interleaved high-voltage gain based on the hybrid switched-capacitor concept is presented. The proposed converter merges interphase transformers and switched-capacitor cells to obtain current sharing and high voltage gain. These features allow for both the reduction of voltage and current stresses on the semiconductors. Furthermore, the interleaving method allows for obtaining multilevel voltages at AC terminals, thereby reducing the weight and bulk of the input inductor. PFC operation is guaranteed through both input current control and output voltage control. Both steady-state and dynamic analyses were conducted. Experimental results for a 1.25 kW, 127 V to 800 V laboratory prototype are presented and discussed.
Output Characteristics Analysis of Energy Harvesting Current Transformer

It is a common power supply method in the power distribution system that energy extracted from the bus current supplies power to the intelligent monitoring devices. The ability to extract energy is directly related with the dimension of the core and the parameters design of the energy harvester. The present literatures only consider the case where the core works on unsaturated region, which rarely consider the case where the core works on saturated region. In this paper, the effects of the core saturation, the voltage ripple of the load, voltage drop of the rectifier diode, winding resistance and the number of turns are fully considered, and the output voltage theoretical model of energy harvester is established. The validity and accuracy of the output voltage theoretical model analysis is verified by experiment and simulation. To maximize the output characteristics of the energy harvester, the capacitance compensation circuit is proposed. When the core is not saturated, the capacitor plays a role of resonance. When the core is saturated, the capacitor provides some negative volt-second product to lift the output voltage. The output voltage theoretical model provides a more complete and effective approach for the optimal design of the energy harvesting current transformer parameters.
Proportional and Reachable Cluster Teleoperation of a Distributed Multi-Robot System

A remote team of robots may be teleoperated by multiple users to explore unstructured environments and to tackle unforeseen emergencies therein. During a large-scale environmental search, each user may visually observe a unique hazard endangering the remote robot connected to their local robot. Therefore, each user may want to tele-drive the remote robot team to a location different than the target locations of other users. This paper resolves the possible conflicts among the multiple user commands through a distributed clustering algorithm that allocates to each user a number of remote robots proportional to the urgency of their request. A pivotal design challenge in the teleoperation context is to ensure that the remote robots allocated to each user are topologically reachable from the user’s local robot within the induced communication subnetwork. The proposed design overcomes this challenge through a reachability-constrained integer linear program that modulates the interconnections of the remote robots on the fly. A comparative experiment on a platform with 2 local and 12 remote robots validates the practical efficacy of the proposed clustering algorithm.
Walking Trajectory Design of Hydraulic Legged Robot with Limited Powered Pump

A hydraulic drive system can generate a large force and is impact resistant. Thus, robots that utilize hydraulic drive systems have been developed for use in disaster areas. In a hydraulic drive system, it is necessary to design the motion of the robot within the capacity of the pump unit, but it is generally difficult to predict the required flow rate and pressure. Especially for hydraulically driven legged robots, the periodic significant changes in the required flow rate and pressure can make it difficult to select a pump and design the leg operation within pump capacity. Therefore, most operations are not designed in full consideration of the characteristics of the pump. By designing the operation of the robot considering the characteristics of the pump, improvements in energy efficiency and working speed can be expected. In this study, we theoretically derive the relationship between the leg motion of the hydraulic leg robot and the required pressure and flow rate. Based on the results, a method for designing the operation considering the characteristics of the pump is proposed, and its validity is experimentally confirmed.
Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.
A Translational Parallel Continuum Robot Reinforced by Origami and Cross-Routing Tendons

We introduce an origami-reinforced parallel continuum robot which is capable of maintaining the orientation of the end effector regardless of the bending shape. The cross-routing tendons provide an effective actuation of the robot because the constant length of the backbones prevent the actuation of parallel arrangement of tendons. We utilise the arclength relationship of parallel curves to show that parallel backbones of equal lengths and constant spacing between the backbones enable the constant orientation of the end effector. The origami shell is introduced to increase the torsional stiffness of the continuum robot and minimise any twisting. This leads to planar and parallel bending of all backbones. Planar quintic Pythagorean Hodograph curves are utilised for shape reconstruction, which is more accurate as compared to the curves with piecewise constant curvatures. The concept of this continuum robot and the accuracy of the reconstruction are validated experimentally.
Monolithically Integrated Narrow Linewidth Semiconductor Laser With a Narrow Band Reflector

We proposed a monolithically integrated narrow linewidth semiconductor laser composing of an active section and a passive narrow band reflector (NBR) based on waveguide Bragg gratings. The π phase shifted Bragg grating is used in the active section to obtain single longitudinal mode output. The NBR is consist of a taper waveguide, π phase shifted antisymmetric Bragg grating (π-PS-ASBG) and uniform Bragg grating. Two sections are integrated on an identical InP wafer and share one continuous waveguide. Because of the hybrid mode resonance in the passive section, it has a relatively long effective length to narrow the linewidth of laser. Narrow linewidth about 16 kHz is obtained when the laser's length is 800 µm and the coupling coefficient of the π-PS-ASBG is 180 cm−1.
Multifunctional dielectric terahertz metasurfaces via spin-decoupled phase control

Integrating multiple functionalities into a single device is a striking field in metasurfaces. Here, we experimentally demonstrate several efficiently multifunctional dielectric metasurfaces in the terahertz regime using a spin-decoupled phase control method allowed by simultaneous control of dynamic phase and geometric phase. The designing strategy shows an impressive robustness and a great flexibility in designing metasurface-based multifunctional devices, and opens new avenues toward the applications in beam steering and polarization multiplexing.
Real-Time Mesh Extraction from Implicit Functions via Direct Reconstruction of Decision Boundary

The ability to estimate 3D object shape from a single image is vital to robotics and manufacturing. For instance, it enables iterative trial-and-error in simulated environments. In single-view reconstruction, implicit functions have demonstrated superior results over traditional methods. However, implicit functions suffer from the heavy computation of mesh extraction. This is due to the indirect mesh extraction, where the number of evaluation points grows cubically with resolution. On the other hand, reducing the resolution results in the discretization error of marching cubes (MC). In this work, we aim to perform efficient and accurate mesh extraction from implicit functions. The idea is to directly reconstruct the decision boundary of implicit functions as a mesh by reverse tracing from the output. It eliminates the need for evaluating massive points and error-prone MC. Consequently, we propose implementing an implicit function via a composite function of a flow and Binary-coded Input Neural Network (BCINN). The boundary of BCINN is easily identifiable, and the flow is invertible. Owing to these properties, the decision boundary of the composite function can be directly and efficiently reconstructed. In our experiments, we demonstrate that the proposed method significantly improves runtime/memory efficiency, with results comparable to those of existing methods. Specifically, our method enables real-time high-quality mesh inference from a single image.
Deep Learning and Mixed Reality to Autocomplete Teleoperation

Teleoperation of robots can be challenging, especially for novice users with little to no experience at such tasks. The difficulty is largely due to the numerous degrees of freedom users must control and their limited perception bandwidth. To help mitigate these challenges, we propose in this paper a solution which relies on artificial intelligence to understand user intended motion and then on mixed reality to communicate the estimated trajectories to the users in an intuitive manner. User intended motion is estimated using a deep learning network trained on a dataset of motion primitives. During teleoperation, the estimated motions are augmented onto a first-person live video feed from the robot. Finally, if a suggested motion is accepted by the user, the robot is driven along that trajectory in an autonomous manner. We validate our proposed mixed reality teleoperation scheme with simulation experiments on a drone and demonstrate, through subjective and objective evaluation, its advantages over other teleoperation methods.
Towards Real-Time Interaction with Industrial Robots in the Creative Industries

This research provides an overview of the challenges towards realizing interactive (near) real-time control of industrial robots within the context of creative industries. Within this field, there an increasing interest in the use of industrial robots for non-standard fabrication, building upon accessible, flow-based visual programming environments.Where robotics middleware is focused on communication, these software tools focus primarily on computational geometry within design software, with the goal of enabling mass-customization and individualization by linking parametric design with toolpath generation and robot simulation.Realizing interactive processes within such environments is challenging, as they form directed dataflows minimizing opportunities for recursive applications and are limited to non-real-time capable operating systems. A series of realized projects is presented that benefits from the advanced geometrical methods provided by the creative industries’ flow-based programming, while at the same time implementing strategies that enable cyclical dataflows and interaction. Novel software interfaces have allowed for fluent interaction with existing robot technologies, even from non-real-time capable environments.
Mechatronic Design of A Low-Noise Active Knee Prosthesis with High Backdrivability

In this paper, we present a low-damping active knee prosthesis (LDKP) with low noise and high backdrivability. The proposed prosthesis is driven by a motor and then decelerated by a four-stage synchronous belt. High backdrivability given by this structure accelerates the prosthetic response. A control system containing several sensors are embedded in the proposed prosthesis to recognize different modes and provide corresponding strategies. Preliminary experiments were carried out on a transfemoral amputee subject, demonstrating the features of low noise, high backdrivability and ability to reproduce natural walking gaits.
Dexterous Manoeuvre through Touch in a Cluttered Scene

Manipulation in a densely cluttered environment creates complex challenges in perception to close the control loop, many of which are due to the sophisticated physical interaction between the environment and the manipulator. Drawing from biological sensory-motor control, to handle the task in such a scenario, tactile sensing can be used to provide an additional dimension of the rich contact information from the interaction for decision making and action selection to manoeuvre towards a target. In this paper, a new tactile-based motion planning and control framework based on bioinspiration is proposed and developed for a robot manipulator to manoeuvre in a cluttered environment. An iterative two-stage machine learning approach is used in this framework: an autoencoder is used to extract important cues from tactile sensory readings while a reinforcement learning technique is used to generate optimal motion sequence to efficiently reach the given target. The framework is implemented on a KUKA LBR iiwa robot mounted with a SynTouch BioTac tactile sensor and tested with real-life experiments. The results show that the system is able to move the end-effector through the cluttered environment to reach the target effectively.
Distributed Full-Consensus Control of Multi-Robot Systems with Range and Field-of-View Constraints

In this paper we solve the full-consensus problem for multiple nonholonomic vehicles interacting over a directed leader-follower topology and subject to sensing constraints in the form of limited range and limited field-of-view. Remarkably, based on a polar-coordinates model transformation, the designed controller is time-invariant and smooth (in the domain of definition). Moreover, the control laws rely only on local measurements, making it well suited for implementation. The asymptotic convergence to the consensus manifold as well as the respect of the constraints is established using Lyapunov’s first method and cascaded systems theory. Realistic simulations in the Gazebo-ROS environment, which illustrate the effectiveness of our theoretical contributions, are shown in an accompanying video.
Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

Reliable and accurate calibration for camera, inertial measurement unit (IMU) and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception. However, traditional calibrations suffer inaccuracy and inconsistency. To address these problems, this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework. In our method, the spatial relationship is geometrically correlated between the sensing units and robotic arm. The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization. Additionally, the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently. The calibration has been evaluated on our developed platform. In the experiments, the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7° and 0.01 m, respectively. The comparisons with state-of-the-art results prove our calibration consistency, accuracy and effectiveness.
Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network

In our research, an adaptive structural learning method of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one of prominent deep learning models. The neuron generation-annihilation in RBM and layer generation algorithms in DBN make an optimal network structure for given input during the learning. In this paper, our model is applied to an automatic recognition method of road network system, called RoadTracer. RoadTracer can generate a road map on the ground surface from aerial photograph data. In the iterative search algorithm, a CNN is trained to find network graph connectivities between roads with high detection capability. However, the system takes a long calculation time for not only the training phase but also the inference phase, then it may not realize high accuracy. In order to improve the accuracy and the calculation time, our Adaptive DBN was implemented on the RoadTracer instead of the CNN. The performance of our developed model was evaluated on a satellite image in the suburban area, Japan. Our Adaptive DBN had an advantage of not only the detection accuracy but also the inference time compared with the conventional CNN in the experiment results. Contribution—The Adaptive DBN showed higher detection accuracy than the conventional CNN for the problem of the road map detection of RoadTracer.
Towards the Unification of System Design and Motion Synthesis for High-Performance Hopping Robots

Robotic hopping requires high performance and precision, due to its extreme interactions with the environment. Designing a system that will perform optimally, or even stably, for this motion primitive is a significant challenge. In previous work, it was shown that designing a robot with two springs (one in series and one in parallel with the actuator) could dramatically improve performance. However, selecting these springs was an intricate process since their dynamics were tightly coupled, and accomplished through trial and error. This work presents a general optimization framework for interconnected systems that designs the time-based hopping motion, while also designing the shape of nonlinear springs on the robot to yield efficient hopping. Utilizing this method, hopping motions and spring designs were generated simultaneously and experimentally verified on a novel hopping robot.
A Real-Time Multi-Task Framework for Guidewire Segmentation and Endpoint Localization in Endovascular Interventions

Real-time guidewire segmentation and endpoint localization play a pivotal role in robot-assisted minimally invasive surgery, which is helpful to reduce radiation dose and procedure time. Nevertheless, the tasks often come with the challenge of limited computational resources. For this purpose, a real-time multi-task framework with two stages is developed. In the first stage, a Fast Attention-fused Network (FAD-Net) is proposed to obtain accurate guidewire segmentation masks. In the second stage, a lightweight localization network and a post-processing algorithm are designed to robustly predict the guidewire endpoint position. Quantitative and qualitative evaluations on intraoperative X-ray sequences from 30 patients demonstrate that the developed framework outperforms the previously-published results for the tasks, achieving state-of-the-art performance. Moreover, the inference rate of the developed framework is approximately 10.6 FPS, which meets the real-time requirement of X-ray fluoroscopy. These results indicate the proposed approach has the potential to be integrated into the robotic navigation framework for endovascular interventions, enabling robotic-assisted minimally invasive surgery.
MCMC Occupancy Grid Mapping with a Data-Driven Patch Prior

Occupancy grids have been widely used for mapping with mobile robots for several decades. Occupancy grids discretize the analog environment and seek to determine the occupancy probability of each cell. More recent occupancy grid mapping algorithms have shown the advantage of capturing cell correlations in the measurement model and the posterior. By estimating the probability of a given map as opposed to a cell, these algorithms have been able to better capture the occupancy probability of cells in the map. The advantage of incorporating data-driven prior probabilities in occupancy grid mapping is explored. A form of Markov Chain Monte Carlo (MCMC) known as Gibbs sampling allows us to sample maps from the full posterior. Previous research has sampled the occupancy probability of each cell, but this paper extends that work to sample a larger patch of cells and highlights the benefit of obtaining the prior for each patch from real maps.
Blockchain for Transacting Energy and Carbon Allowance in Networked Microgrids

This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints.