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In this paper, a new simple pulse multiplication circuit (SPMC) with low current stress is proposed, in order to upgrade a normal 20-pulse autotransformer rectifier (20PAR) to a 40PAR. The proposed SPMC comprises two tapped inter-phase reactors (TIPRs) and two additional diodes with lower conduction losses. The proposed SPMC does not require a zero-sequence blocking transformer (ZSBT) compared to conventional pulse multiplication circuits, which leads to simplicity in implementation. Simulation and experimental results show that the total harmonic distortion (THD) of the input current using 40PAR is less than 3%, which meets the DO-160G requirements for aviation applications.

This paper investigates a high power-factor permanent magnet vernier machine (PMVM) equipped with low-coupling hybrid concentrated-winding (CW). The proposed hybrid-CW, carrying both star- and delta-winding sets, exhibits a good filtering property to both sub- and super-order harmonics. Through the meticulous design of the short coil pitch, the ratio of inductance to magnet flux linkage is decreased, leading to a great improvement in power factor. The proposed low-coupling winding design contributes to further power factor improvement by reducing the inductance while retaining the magnet flux linkage. It is revealed that the mutual coupling between different coils of single phase and that between different windings of three phases are suppressed significantly in the hybrid-CW, thus leading to high power factor and potentially high fault tolerance. Finite element results show that the proposed hybrid-CW PMVM exhibits a significantly improved power factor up to 0.96 from 0.83 and 0.75, as compared with two counterpart PMVMs with open-slot and split-tooth structures, respectively. Benefiting from the magnetic gearing effect, the proposed PMVM has a promising active torque density of 40 Nm/L. Taking end-winding volume into consideration, the proposed PMVM exhibits an actual torque density of 21.98 Nm/L, which is 22.52% and 52.43% higher than the investigated open-slot and split-tooth counterpart PMVMs. Finally, a prototype is fabricated and tested to validate the high-power-factor and high-torque-density features of the proposed hybrid-CW PMVM. National Research Foundation (NRF) Submitted/Accepted version This work was supported by the National Research Foundation (NRF) Singapore under its NRF fellowship Grant NRF-NRFF12-2020-0003.

The initial alignment provides an accurate attitude for SINS (strapdown inertial navigation system). By further estimating the IMU's bias and misalignment angle, the recursive Bayesian filter is accurate. However, the prior heading error has significant influence on the convergence speed and accuracy. In addition, the accuracy will be limited by its iteration at a single time-step. Coarse alignment method OBA (optimization-based alignment) uses MLE (maximum likelihood estimation) to find the optimal attitude quickly. However, few methods consider the IMU bias and misalignment angle, which will reduce the attitude accuracy. In this paper, a unified method based on FGO (Factor graph optimization) and IBF (inertial base frame) is proposed. The attitude is estimated by MLE, IMU bias and misalignment angle are estimated by MAP estimation. The state of all time steps is optimized together to further improve the accuracy. Physical experiments on the rotation MEMS SINS show that the heading accuracy of this method is improved in limited alignment time., 8 pages, This article has been accepted for publication in IEEE Transactions on Industrial Electronics

The modular multilevel dc-dc converters (MMDCs) have attracted much interest in medium voltage dc applications, but have to face the main issue of balancing submodule (SM) capacitor voltages. This paper proposes a dual-circulant modulation and proves it possessing the inherent balancing capability for arbitrary operation cases. High-speed communication for real-time sensor data transfer could be avoided, which reduces implementation costs and also enhances the system reliability. Two sets of circular switching patterns are preset and combined to complete the full constrains on SM voltages. The associated polynomial of circulant matrix is introduced and the coprime of polynomials demonstrates the full-rank feature of the extended switching matrix that promises the inherent balancing for any operation cases. Then the switching patterns are reallocated and optimized to keep the SM uniformity and reduce the capacitor voltage ripple at the same time. Full-scale simulations on MMDC models and down-scaled experiments on prototypes are both presented, which validates the inherent voltage balancing capability and the optimization of SM capacitor voltage ripple with the proposed dual-circulant modulation.

This paper proposes a double-sided flux concentrated permanent magnet linear machine (DS-FCPMLM) with saturation relieving effect. With the adoption of slot PMs, DS-FCPMLM can effectively relieve the primary saturation and greatly improve the thrust force density. First, the machine topology and operation principle are introduced. Then, the flux generation mechanism under dual PMs and complementary structure is analytically calculated based on magnetomotive force-permeance model, and further verified by finite element analysis. In addition, some electromagnetic performances including open-circuit characteristic, thrust force performances, demagnetization risk and power factor are comparatively studied. With the help of slot PMs, DS-FCPMLM can significantly improve the thrust force density by 63.4% compared with that only has yoke PMs, and the overload capability can be extended to nearly 4 times of the rated value. More importantly, DS-FCPMLM only consumes 1/30 PM volumes while can achieve 72.2% thrust force density compared with conventional PMLMs for a long stroke with 10 meters. Finally, a prototype of the DS-FCPMLM is carried out for experimental validation. National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by the National Research Foundation (NRF) Singapore under its NRF Fellowship Grant NRF-NRFF12-2020-0003 and in part by the National Natural Science Foundation of China under Grant 52107060 and Grant 52177061.

About one hundred magnets of six different types shall be installed in the High Luminosity LHC (HL-LHC) in the years 2026-2028 at CERN. The magnets design, construction and test are based on CERN collaborations with institutes and industrial partners in USA, Spain, Italy, Japan and China. Three types of correctors are based on Nb‒Ti technology and feature conductor peak fields in the 2 to 4 T range: for all of them the prototype phase has been successfully completed. The production is well advanced for the superferric correctors, and is starting for the canted cosine theta correctors and for the nested correctors. The separation and recombination Nb‒Ti dipoles D1 and D2, with a 4.5-6 T bore field range, are both in the prototype phase after the completion of the short model program. The most challenging magnet, the Nb3Sn quadrupole with conductor peak field above 11 T, is in the prototype phase at CERN and halfway through the production phase in the USA. In this paper we will give, for each type of magnet, an overview of the main achievements obtained so far and we will outline the technical points still needing validation from the prototype program.

When simulating no-insulation high-temperature superconducting pancake coils with the finite element (FE) method, the high aspect ratio of the thin turn-to-turn contact layer (T2TCL) leads to unfavorable meshes in these thin layers as manifested by a high number of degrees of freedom (DoF) or mesh elements of poor quality which decrease the accuracy of the simulation results. To mitigate this issue, we propose to collapse the T2TCL volume into a surface using a thin shell approximation (TSA) for three-dimensional FE analysis. A $\vec{H}-\phi$formulation is used and solves for the magnetic field strength $\vec{H}$in conducting domains and the magnetic scalar potential $\phi$in insulating domains. This formulation avoids spurious currents and reduces the number of DoF in insulating domains. Automatically created thick cuts are used to deal with multiply connected domains. Particular attention is paid to the interpretation of these cuts and the corresponding basis functions in the context of pancake coil geometries. The efficiency of the formulation facilitates the resolution of each turn. In this way, local phenomena such as quench can be captured in a straightforward way. The TSA formulation is verified by comparison against a reference model with volumetrically meshed T2TCL and is shown to be accurate and efficient, significantly reducing the solution time while reducing the effort for creating high-quality meshes. The TSA is implemented in an open-source FE framework and the source code is shared alongside this paper. When simulating no-insulation high-temperature superconducting pancake coils with the finite element (FE) method, the high aspect ratio of the thin turn-to-turn contact layer (T2TCL) leads to unfavorable meshes in these thin layers as manifested by a high number of degrees of freedom (DoF) or mesh elements of poor quality which decrease the accuracy of the simulation results. To mitigate this issue, we propose to collapse the T2TCL volume into a surface using a thin shell approximation (TSA) for three-dimensional FE analysis. A H⃗ −ϕ formulation is used and solves for the magnetic field strength H⃗ in conducting domains and the magnetic scalar potential ϕ in insulating domains. This formulation avoids spurious currents and reduces the number of DoF in insulating domains. Automatically created thick cuts are used to deal with multiply connected domains. Particular attention is paid to the interpretation of these cuts and the corresponding basis functions in the context of pancake coil geometries. The efficiency of the formulation facilitates the resolution of each turn. In this way, local phenomena such as quench can be captured in a straightforward way. The TSA formulation is verified by comparison against a reference model with volumetrically meshed T2TCL and is shown to be accurate and efficient, significantly reducing the solution time while reducing the effort for creating high-quality meshes. The TSA is implemented in an open-source FE framework and the source code is shared alongside this paper.

Stainless steel vessels see widespread use in superconducting magnets for particle accelerator applications. Their function varies in different magnet designs: they always provide the necessary liquid helium containment, but in some cases are also used to provide azimuthal prestress and can also be welded to the magnet end plate to provide additional longitudinal stiffness. A magnet designed with the bladder and key technology does not rely on the structural role of the vessel. They are structurally supported using azimuthally prestressed aluminum shells, and the longitudinal constraint by rods. In this case, the magnet designer would generally like to minimize the interaction between the magnet and the stainless-steel vessel and to minimize the coil stress variation due to the vessel. The stress state in the vessel and in the coil is a function of the circumferential interference, defined as the vessel azimuthal length minus the magnet circumference. The vessel and the magnet azimuthal length machining tolerances are relatively large resulting in significant stress variations in the superconducting coils. In this paper we introduce an interference control shim, which can significantly limit the stress variation of the coils for a given variation of the interference. The effectiveness of the interference-control shim is evaluated numerically on the MQXF, the low-β quadrupole for the High Luminosity LHC.

Multilayer 2G high-temperature superconductor (HTS) tapes undergo various mechanical loading steps as part of a superconducting magnet operation. The mechanical loading steps include cool-down to cryogenic temperatures and Lorentz forces when powering up the magnets. Studying the mechanical behavior of the constituent layers in a HTS tape can reveal the strain or stress level in each layer and help in predicting the probability of mechanical failure or critical current degradation. A detailed finite element method (FEM) -based simulation models enable us to estimate the mechanical behavior in each layer. However, defining the model parameters for material mechanical properties or thickness of each material layer can have consid-erable uncertainty due to variation in manufacturing processes and missing measurements of material properties within these tapes. The material properties in thin layers may not exactly comply with bulk materials of the same kind. In this paper we present a sensitivity analysis for mechanical behavior dependence on material properties variation in the constituent layers of a HTS tape. The results give important insight about the accuracy and reliability of mechanical simulation results and guide the priorities in future material characterization. In addition, we will investigate the effect of the thickness of Hastelloy layer on effective macroscopic mechanical behavior of the tape. A nonlinear elastoplastic FEM model is used for performing the simulations. publishedVersion

International audience; Hybrid magnets are currently under consideration as an economically viable option towards 20 T dipole magnets for next generation of particle accelerators. In these magnets, High Temperature Superconducting (HTS) materials are used in the high field part of the coil with so-called insert coils, and Low Temperature Superconductors (LTS) like Nb3Sn and Nb-Ti superconductors are used in the lower field region with so-called outsert coils. The attractiveness of the hybrid option lays on the fact that, on the one hand, the 20 T field level is beyond the Nb3Sn practical limits of 15-16 T for accelerator magnets and can be achieved only via HTS materials; on the other hand, the high cost of HTS superconductors compared to LTS superconductors makes it advantageous exploring a hybrid approach, where the HTS portion of the coil is minimized. We present in this paper an overview of different design options aimed at generating 20 T field in a 50 mm clear aperture. The coil layouts investigated include the Cos-theta design (CT), with its variations to reduce the conductor peak stress, namely the Canted Cos-theta design (CCT) and the Stress Management Cos-theta design (SMCT), and, in addition, the Block-type design (BL) including a form of stress management and the Common-Coil design (CC). Results from a magnetic and mechanical analysis are discussed, with particular focus on the comparison between the different options regarding quantity of superconducting material, field quality, conductor peak stress, and quench protection.

Large-scale electric aircraft is a disruptive technology to address the environmental impact of air travel. Fault current limitation is crucial to realise the safety and reliability of the electric aircraft, in particular for large-scale electric aircraft using DC distribution network. This paper investigates the behavior of the resistive SFCL under different cryogenic cooling systems including liquid nitrogen and helium gas circulation cooling systems for the electric aircraft DC distribution network. Electromagnetic and thermoelectric models were used to predict the characteristics of resistive SFCL. The simulation results demonstrate that it is promising to design resistive SFCL using liquid nitrogen cooling as well as helium gas circulation cooling systems, which offer flexibility for SFCL cryogenic cooling system for electric aircraft applications.

In this paper, a Phase-shift-modulated (PS) Resonant Two-switch Boosting Switched-capacitor Converter (RTBSC) and its modulation map are proposed. Compared with the traditional RTBSC, all diodes are replaced by active switches and then phase-shift modulation is applied. The advantages of the modified topology are obtained as follow. 1) The voltage gain can be regulated efficiently either below or above the nominal value even at light-load condition, making it suitable for applications with voltage fluctuations; 2) The conduction loss is reduced by replacing all diodes in RTBSC with low turn-on resistance transistors, especially for high-order and high-gain configuration; 3) All switches achieve the zero-voltage-switching (ZVS) turn-on operation; 4) The soft-charging property smooths the current profile. Aside from the topology modification, a comprehensive analysis of operation principle, voltage gain, component stress and ZVS region is given. On the basis, a modulation map with the optimal operation region is proposed. It provides a guideline to operate and design such converters in the target gain range, low component stress and ZVS region, making up for the lack of modulation map and design guideline in aforementioned literatures. A 120W prototype with low-voltage side 43-60V and high-voltage side 150V was designed to verify the above analyses.

In many grid synchronization systems (GSSs), amplitude and phase estimation loops are dynamically coupled. It means that a change in the input voltage amplitude may result in spurious phase/frequency transients in the GSS output, and vice versa. This paper aims to explain the reason behind this coupling and present some guidelines to mitigate it. Several case studies are provided and numerical and experimental results are presented to make all this more clear.

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

Secondary arc extinction time is an important factor, which influences system stability because it directly affects the reclosing time. However, few reports exist on a field data of the secondary arc extinction time on transmission lines. This paper describes the investigation results of secondary arc extinction characteristics by analyzing voltage wave shapes during the lightning faults on 550 kV transmission lines. Recovery voltages and secondary arc currents on each transmission line during faults were calculated to consider correlation with the secondary arc extinction time. The secondary arc extinction time is estimated by the existing formula, the secondary arc current and the recovery voltage required for one second reclosing are evaluated.

This paper proposes a distributed intelligent secondary control (SC) approach based on brain emotional learning-based intelligent controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC controller is able to learn quick-auto and handle model complexity, non-linearity, and uncertainty of the MG. The proposed controller is fully model-free, indicating that the voltage amplitude and frequency deviations are regulated without previous knowledge of the system model and parameters. This approach ensures low steady-state variations with higher bandwidth and maintains accurate power-sharing of the droop mechanism. Furthermore, primary control is realized with a robust finite control set-model predictive control (FCS-MPC) in the inner level to increase the system frequency bandwidth and a droop control in the outer level to regulate the power-sharing among the distributed generations. Finally, experimental tests obtained from a hardware-in-the-loop testbed validate the proposed control strategy for different cases.

This paper investigates a new method to model and control variable-frequency power converters in a switching-synchronized sampled-state space for cycle-by-cycle digital control. There are a number of significant benefits in comparison to other methods including fast dynamic performance together with ease of design and implementation. Theoretical results are presented and verified through hardware, and simulations of a current-mode buck converter with constant on-time and a current-mode boost converter with constant off-time. Dynamic voltage scaling for microprocessors and LiDAR are among the applications that can benefit.

Implementing control schemes for modular multilevel converters (M2Cs) involves both a cyber and a physical level, leading to a cyber-physical system (CPS). At the cyber level, a communication network enables the data exchange between sensors, control platforms, and monitoring systems. Meanwhile, at the physical level, the semiconductor devices that comprise the M2C are switched ON/OFF by the control system. In this context, almost all published works in this research area assume that the CPS always reports correct information. However, this may not be the case when the M2C is affected by cyber-attacks, such as the one named false data injection attack (FDIA), where the data seen by the control system is corrupted through illegitimate data intrusion into the CPS. To deal with this situation, FDIA detectors for the M2C are recently starting to be studied, where the goal is to detect and mitigate the attacks and the attacked sub-modules. This paper proposes a reinforcement learning (RL)-based method to uncover the deficiencies of existing FDIAs detectors used for M2C applications. The proposed method auto-generates complex attack sequences able to bypass FDIA detectors. Therefore, it points out the weaknesses of current detectors: This valuable information can be used later to improve the performance of the detectors, establishing more reliable cybersecurity solutions for M2Cs. The RL environment is developed in Matlab/Simulink augmented by PLECS/blockset, and it is made available to researchers on a website to motivate future research efforts in this area. Hardware-in-the-loop (HIL) studies verify the proposal's effectiveness.

Feature related particle data analysis plays an important role in many scientific applications such as fluid simulations, cosmology simulations and molecular dynamics. Compared to conventional methods that use hand-crafted feature descriptors, some recent studies focus on transforming the data into a new latent space, where features are easier to be identified, compared and extracted. However, it is challenging to transform particle data into latent representations, since the convolution neural networks used in prior studies require the data presented in regular grids. In this paper, we adopt Geometric Convolution, a neural network building block designed for 3D point clouds, to create latent representations for scientific particle data. These latent representations capture both the particle positions and their physical attributes in the local neighborhood so that features can be extracted by clustering in the latent space, and tracked by applying tracking algorithms such as mean-shift. We validate the extracted features and tracking results from our approach using datasets from three applications and show that they are comparable to the methods that define hand-crafted features for each specific dataset.

Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication security is effective in identifying falsified and malicious V2X data. Each vehicle in a given region can be a witness to report on the misbehavior of other nearby vehicles, which will then be added to a "blacklist." However, there may not exist enough witness vehicles that are willing to opt-in in the early stage of connected-vehicle deployment. In this paper, we propose a "whitelisting" approach to V2X security, titled Proof-of-Travel (POT), which leverages the support of roadside infrastructure. Our goal is to transform the power of cryptography techniques embedded within Vehicle-to-Infrastructure (V2I) protocols into game-theoretic mechanisms to incentivize connected-vehicle data sharing and validate data trustworthiness simultaneously. The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra costs for normal vehicles, which naturally want to move from the origin to the destination. The POT protocol is used to enhance the security of previous voting-based data validation algorithms for V2I crowdsensing applications. For the POT-enhanced voting, we prove that all vehicles choosing to cheat are not a pure Nash equilibrium using game-theoretic analysis. Simulation results suggest that the POT-enhanced voting is more robust to malicious data., Preprint, IEEE Internet of Things Journal

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18dB., Comment: To appear in the IEEE Internet of Things Journal

The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly in healthcare, energy, transportation, etc. However, data collection may have to be outsourced to external service providers (SPs) due to cost considerations or lack of specialized equipment. Hence, the data market plays a critical role in such scenarios where SPs have different quality levels of available data, and IoT users have different application-specific data needs. The pairing between data available to the SP and users in the data market requires an effective mechanism design that considers the SPs' profitability and the quality-of-service (QoS) needs of the users. We develop a generic framework to analyze and enable such interactions efficiently, leveraging tools from contract theory and mechanism design theory. It can enable and empower emerging data sharing paradigms such as Sensing-as-a-Service (SaaS). The contract design creates a pricing structure for on-demand sensing data for IoT users. By considering a continuum of user types, we capture a diverse range of application requirements and propose optimal pricing and allocation rules that ensure QoS provisioning and maximum profitability for the SP. Furthermore, we provide analytical solutions for fixed distributions of user types to analyze the developed approach. For comparison, we consider the benchmark case assuming complete information of the user types and obtain optimal contract solutions. Finally, a case study is presented to demonstrate the efficacy of the proposed contract design framework., Comment: This paper has been accepted for publication in the IEEE Internet of Things Journal

Markerless human motion capture (mocap) from multiple RGB cameras is a widely studied problem. Existing methods either need calibrated cameras or calibrate them relative to a static camera, which acts as the reference frame for the mocap system. The calibration step has to be done a priori for every capture session, which is a tedious process, and re-calibration is required whenever cameras are intentionally or accidentally moved. In this paper, we propose a mocap method which uses multiple static and moving extrinsically uncalibrated RGB cameras. The key components of our method are as follows. First, since the cameras and the subject can move freely, we select the ground plane as a common reference to represent both the body and the camera motions unlike existing methods which represent bodies in the camera coordinate. Second, we learn a probability distribution of short human motion sequences ($\sim$1sec) relative to the ground plane and leverage it to disambiguate between the camera and human motion. Third, we use this distribution as a motion prior in a novel multi-stage optimization approach to fit the SMPL human body model and the camera poses to the human body keypoints on the images. Finally, we show that our method can work on a variety of datasets ranging from aerial cameras to smartphones. It also gives more accurate results compared to the state-of-the-art on the task of monocular human mocap with a static camera. Our code is available for research purposes on https://github.com/robot-perception-group/SmartMocap.

We propose an extension to the MAPF formulation, called SocialMAPF, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and corridor intersections. SocialMAPF is able to, for instance, accurately reason about the urgent incentive of an agent rushing to the hospital over another agent's less urgent incentive of going to a grocery store; MAPF ignores such agent-specific incentives. Our proposed formulation addresses the open problem of optimal and efficient path planning for agents with private incentives. To solve SocialMAPF, we propose a new class of algorithms that use mechanism design during conflict resolution to simultaneously optimize agents' private local utilities and the global system objective. We perform an extensive array of experiments that show that optimal search-based MAPF techniques lead to collisions and increased time-to-goal in SocialMAPF compared to our proposed method using mechanism design. Furthermore, we empirically demonstrate that mechanism design results in models that maximizes agent utility and minimizes the overall time-to-goal of the entire system. We further showcase the capabilities of mechanism design-based planning by successfully deploying it in environments with static obstacles. To conclude, we briefly list several research directions using the SocialMAPF formulation, such as exploring motion planning in the continuous domain for agents with private incentives., Full paper submission to R-AL. Shorter Blue Sky paper version available at AIHRI/2022/1016

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This paper presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching., Comment: 8 pages, 8 figures, 4 tables, the proofread version will appear on RA-L soon

Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in weakly textured or motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and NetVLAD. To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR.

Robotic detection of people in crowded and/or cluttered human-centered environments including hospitals, long-term care, stores and airports is challenging as people can become occluded by other people or objects, and deform due to variations in clothing or pose. There can also be loss of discriminative visual features due to poor lighting. In this paper, we present a novel multimodal person detection architecture to address the mobile robot problem of person detection under intraclass variations. We present a two-stage training approach using 1) a unique pretraining method we define as Temporal Invariant Multimodal Contrastive Learning (TimCLR), and 2) a Multimodal Faster R-CNN (MFRCNN) detector. TimCLR learns person representations that are invariant under intraclass variations through unsupervised learning. Our approach is unique in that it generates image pairs from natural variations within multimodal image sequences, in addition to synthetic data augmentation, and contrasts crossmodal features to transfer invariances between different modalities. These pretrained features are used by the MFRCNN detector for finetuning and person detection from RGB-D images. Extensive experiments validate the performance of our DL architecture in both human-centered crowded and cluttered environments. Results show that our method outperforms existing unimodal and multimodal person detection approaches in terms of detection accuracy in detecting people with body occlusions and pose deformations in different lighting conditions.

Many animals can move in cluttered environments by conforming their body shape to geometric constraints in their surroundings such as narrow gaps. Most robots are rigid structures and do not possess these capabilities. Navigation around movable or compliant obstacles results in a loss of efficiency—and possible mission failure—compared to progression through them. In this paper, we propose the novel design of a deformable mobile robot; it can adopt a wider stance for greater stability (and possible higher payload capacity), or a narrower stance to become capable of fitting through small gaps and progressing through flexible obstacles. We use a whisker-based feedback control approach in order to match the amount of the robot's deformation with the compliance level of the obstacle. We present a real-time algorithm which uses whisker feedback and performs shape adjustment in uncalibrated environments. The developed robot was tested navigating among obstacles with varying physical properties from different approach angles. Our results highlight the importance of co-development of environment perception and physical reaction capabilities for improved performance of mobile robots in unstructured environments.

Model Predictive Control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-Linear Parameter Varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables; these scheduling parameters are a-priori unknown along a future prediction horizon, which complicates MPC design. To address this problem, literature points out two options: robust MPC approaches, considering the scheduling to be uncertain; or sub-optimal ones, that set values for these parameters along the horizon. With respect to the latter group, this paper proposes an extrapolation algorithm that estimates the future values of the qLPV scheduling parameters for a fixed prediction horizon of N steps; the method is based on a recursive procedure using simple Taylor expansions. Sufficient conditions for convergent extrapolation are presented with regard to the form and class of the scheduling function and the robustness of the MPC. Different benchmark examples from the literature are presented to illustrate the algorithm, which is also compared to state-of-the-art techniques.

In this paper, we address the efficient implementation of moving horizon state estimation of constrained discrete-time linear systems. We propose a novel iteration scheme which employs a proximity-based formulation of the underlying optimization algorithm and reduces computational effort by performing only a limited number of optimization iterations each time a new measurement is received. We outline conditions under which global exponential stability of the underlying estimation errors is ensured. Performance guarantees of the iteration scheme in terms of regret upper bounds are also established. A combined result shows that both exponential stability and a sublinear regret which can be rendered smaller by increasing the number of optimization iterations can be guaranteed. The stability and regret results of the proposed estimator are showcased through numerical simulations.

The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces, that is, their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This paper proposes a novel filter, the Equivariant Filter (EqF), by posing the observer state on the symmetry group, linearising global error dynamics derived from the equivariance of the system, and applying extended Kalman filter design principles. We show that equivariance of the system output can be exploited to reduce linearisation error and improve filter performance. Simulation experiments of an example application show that the EqF significantly outperforms the extended Kalman filter and that the reduced linearisation error leads to a clear improvement in performance., 19 pages, 3 figures, submitted to IEEE TAC

Signal Temporal Logic (STL) has been found an expressive language for describing complex, time-constrained tasks in several robotic applications. Existing methods encode such specifications by either using integer constraints or by employing set invariance techniques. While in the first case this results in MILP control problems, in the latter case designer-specific choices may induce conservatism in the robot's performance and the satisfaction of the task. In this paper a continuous-time receding horizon control scheme (RHS) is proposed that exploits the trade-off between task satisfaction and performance costs such as actuation and state costs, traditionally considered in RHS schemes. The satisfaction of the STL tasks is encoded using time-varying control barrier functions (CBFs) that are designed online, thus avoiding the integer expressions that are often used in literature. The recursive feasibility of the proposed scheme is guaranteed by the satisfaction of a time-varying terminal constraint that ensures the satisfaction of the task with pre-determined robustness. The effectiveness of the method is illustrated in a multi-robot simulation scenario. QC 20230706

Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is either assumed to be known a priori or estimated from data through set-membership identification. However, the problem of computing a suitable input disturbance set in case the set of possible output values is assigned a priori has received relatively little attention. This problem arises in many contexts, such as in supervisory control, actuator design, decentralized control, and others. In this paper, we propose a method to compute input disturbance sets (and the corresponding set of states) such that the resulting set of outputs matches as closely as possible a given set of outputs, while additionally satisfying strict (inner or outer) inclusion constraints. We formulate the problem as an optimization problem by relying on the concept of robust invariance. The effectiveness of the approach is demonstrated in numerical examples that illustrate how to solve safe reference set and input-constraint set computation problems.

Linear systems over the max-plus algebra provide a suitable formalism to model discrete event systems where synchronization, without competition, is involved. In this paper, we consider a formulation of the model matching problem for systems of such class, in which the output of a given system, called the plant, is forced, by a suitable input, to track exactly that of a given model. A necessary and sufficient condition for its solvability is obtained by making a suitable use of geometric methods in the framework of systems over the max-plus algebra.

In this paper, a novel control strategy namely disturbance observer-based control is first applied to stabilization and disturbance rejection for an anti-stable stochastic heat equation with Neumann boundary actuation and unknown boundary external disturbance generated by an exogenous system. A disturbance observer-based boundary control is designed based on the backstepping approach and estimation/cancellation strategy, where the unknown disturbance is estimated in real time by a disturbance observer and rejected in the closed-loop, while the in-domain multiplicative noise whose intensity is within a known finite interval is attenuated. It is shown that the resulting closed-loop system is exponentially stable in the sense of both mean square and almost surely. A numerical example is demonstrated to validate the effectiveness of the proposed control approach.

Given a stochastic dynamical system modelled via stochastic differential equations (SDEs), we evaluate the safety of the system through characterizations of its exit time moments. We lift the (possibly nonlinear) dynamics into the space of the occupation and exit measures to obtain a set of linear evolution equations which depend on the infinitesimal generator of the SDE. Coupled with appropriate semidefinite positive matrix constraints, this yields a moment-based approach for the computation of exit time moments of SDEs with polynomial drift and diffusion dynamics. However, the existing moment approach suffers from drawbacks which impede its applicability to the analysis of higher dimensional physical systems. To apply the existing approach, the dynamics of the systems are limited to polynomials of the state - excluding a large majority of real world examples. Computational scalability is also poor as the dimensionality of the state increases, largely due to the combinatorial growth of the optimization program. In this paper, we propose changes to the existing moment method to make feasible the safety analysis of higher dimensional physical systems. The restriction to polynomial dynamics is lifted by using a state augmentation method which allows one to generate the evolution equations for a broader class of nonlinear stochastic systems. We then reformulate the constraints of the optimization to mitigate the computational limitations associated with an increase in state dimensionality. We employ our methodology on two example processes to characterize their safety via expected exit times and demonstrate the ability to handle multi-dimensional physical systems that were previously unsupported by the existing SDP method of moments.

This paper investigates two ways of output-power computation, namely, sparse- and multi-sampling-based methods, to overcome sampling speed limitation and arcing nonlinearity for electrosurgery. Moreover, an impedance-based power adaptation strategy is explored for reduced collateral tissue damage.The efficacy of the proposed power computation and adaptation strategy are experimentally investigated on a gallium-nitride (GaN)-based high-frequency inverter prototype that allows electrosurgery with a 390 kHz output frequency.The sparse-sampling-based method samples output voltage once and current twice per cycle. The achieved power computing errors over 1000 cycles are 1.43 W, 2.54 W, 4.53 W, and 4.89 W when output power varies between 15 W and 45 W. The multi-sampling-based method requires 28 samples of both outputs, and the corresponding errors are 0.02 W, 0.86 W, 1.86 W, and 3.09 W. The collateral tissue damage gauged by average thermal spread is 0.86 mm, 0.43 mm, 1.11 mm, and 0.36 mm for the impedance-based power adaptation against 1.49 mm for conventional electrosurgery.Both power-computation approaches break sampling speed limitations and calculate output power with small errors. However, with arcing nonlinearity presence, the multi-sampling-based method yields better accuracy. The impedance-based power adaptation reduces thermal spreads and diminishes sensor count and cost.This paper exemplifies two novel power-computation ways using low-end industrial-scale processors for biomedical research involving high-frequency and nonlinearly distorted outputs. Additionally, this work is the first to present the original impedance-based power adaptation strategy for reduced collateral damage and it may motivate further interdisciplinary research towards collateral-damage-less electrosurgery.

The \emph{MIDNA} application specific integrated circuit (ASIC) is a skipper-CCD readout chip fabricated in a 65 nm LP-CMOS process that is capable of working at cryogenic temperatures. The chip integrates four front-end channels that process the skipper-CCD signal and performs differential averaging using a dual slope integration (DSI) circuit. Each readout channel contains a pre-amplifier, a DC restorer, and a dual-slope integrator with chopping capability. The integrator chopping is a key system design element in order to mitigate the effect of low-frequency noise produced by the integrator itself, and it is not often required with standard CCDs. Each channel consumes 4.5 mW of power, occupies 0.156 mm${^2}$ area and has an input referred noise of 2.7${\mu\nu}_{rms}$. It is demonstrated experimentally to achieve sub-electron noise when coupled with a skipper-CCD by means of averaging samples of each pixel. Sub-electron noise is shown in three different acquisition approaches. The signal range is 6000 electrons. The readout system achieves 0.2${e^{-}}$ RMS by averaging 1000 samples with MIDNA both at room temperature and at 180 Kelvin.