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This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data.

Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when initial error is large and overlapping area is low. In this paper, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. Firstly, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using Expectation-Maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on Semantic KITTI benchmarks and real world experiments show the improved accuracy, convergence, and robustness.

Recently, intelligent unmanned aerial vehicles (UAVs) have shown great advantages of flexibility and productivity in power line inspection, wherein robust detection of power lines from aerial images for automatic power line following navigation is required. However, identifying power lines accurately from a cluttered background is challenging due to the limited resolution of onboard cameras and the noisy environment. In this letter, we propose a novel power line detection method, denoted by CODNet, for the application of UAV navigation. Unlike existing works, the proposed method can extract features of power lines from cluttered backgrounds automatically and predict centers and orientations of power lines in the scene simultaneously. Besides, we introduce a new clustering method to summarize the average location and orientation of detected power lines as a guide for the automatic navigation of UAVs. Finally, experimental results demonstrate both the effectiveness and the superiority of the CODNet. This work was supported in part by the Natural Science Fund of China (NSFC) under Grant 51575186; in part by the Shanghai Science and Technology Action Plan under Grant 18DZ1204000, Grant 18510745500, Grant 18510750100, and Grant 18510730600; and in part by the Shanghai Aerospace Science and Technology Innovation Fund (SAST) under Grant 2019-080 and Grant 2019-116.

Stereo matching is a practical method to estimate depth information and retrieve 3D world in robot perception and autonomous driving scenarios. With the development of convolution neural networks (CNNs), deep-learning based stereo matching algorithms have significantly improved the accuracy and dominated most of the online benchmarks. However, limited labels in real world, especially in challenging weather conditions, still hinder the technology from practical usage. In this paper, we propose a new unsupervised learning mechanism for stereo matching, utilizing adversarial iterative learning and novel soft warping loss to promote the effectiveness of the networks in unseen environments. The experiments transferring the stereo matching module from synthetic domain to real-world domain demonstrate the superiority of our proposed method. Extensive experiments in challenging weathers further prove that our method shows great practical potential in strait environments.

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.

This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. Unlike most of the formation flying related work, this article considers a more practical issue that some of the vehicles may break down during operation. In this case, a more reasonable coping strategy is proposed which is to reconstruct the formation such that the task objectives can be achieved optimally. To perform the patrol task, a virtual target is introduced which moves along the patrol path with a predetermined speed. Considering the fact that not all the vehicles have access to the patrol path information, a decentralized estimator is designed for each vehicle to estimate the virtual target state respectively based on which the individual reference trajectories can be generated. As these reference trajectories do not satisfy relevant physical constraints, including system model, control input limits, no-fly zone avoidance, and intervehicle collision avoidance, a novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which is computationally attractive by incorporating a situation-dependent mechanism. Simulations have been conducted to validate the effectiveness of our proposed algorithm.

The problem of flexible spacecraft attitude tracking control with guaranteeing the prescribed performance is investigated. First, the Lagrangian model of the flexible spacecraft is derived. Then, a model-free control law is proposed, in which information of spacecraft and flexible appendage is not required. The proposed control law is able to guarantee the prescribed transient and steady-state behavior for both attitude and angular velocity errors in the presence of bounded external disturbances. The stability of the resulting closed-loop system is guaranteed by the Lyapunov approach. Two special forms of the proposed control law are subsequently obtained. The effectiveness of both complete form and special forms of the proposed control law is verified by numerical simulations. Since the modal information of spacecraft is not involved in the control design, the proposed control law can also be used for rigid spacecraft.

Repetitive or ambiguous environment, where structures are highly similar and distinct geometric features are not sufficient, is one of the critical and challenging scenarios for mobile robots to perform global localization or simultaneous localization and mapping (SLAM) tasks. The robots are easy to get lost or mismatched to wrong places in such environments. The existing solutions either rely heavily on pre-installed infrastructures which are inflexible and expensive or rely on single sensor-based global localization methods whose initialization module is not capable enough to provide distinctive information. Thus, this article proposes a hierarchical probabilistic framework that addresses the problem of infrastructure-free mobile robot global localization in GPS-challenged repetitive environments by leveraging both the measured magnetic field (MF) and Light Detection and Ranging (LiDAR) information. The proposed hierarchical system mainly consists of: 1) coarse localization: MF-based localization and 2) fine localization: LiDAR-based localization (LBL). The ambient MF functions as a substitute for the GPS signal, which is considered not available or severely challenged in indoor/semi-indoor local environments. Based on the pre-built MF database, multiple coarse candidate robot poses can first be determined by the proposed initial pose estimation algorithm, which incorporates multivariate Gaussian observation model and random sample consensus (RANSAC) algorithm. Then, using the obtained multiple candidate poses as initialization, the robot can be localized more accurately by LiDAR-based fine localization. Extensive real-world experiments demonstrate that the proposed system achieves over 95% localization success rate and takes less than 2.0-m average traveling distance to localize the mobile robot.

Adversarial examples can be imperceptible to human eyes but can easily fool deep models. Such intrigue property has raised security issues for real-world industrial deep learning systems. To combat those malicious attacks, a novel defense strategy has been proposed based on the conditional variational autoencoder (CVAE) and Bayesian network (BN). The main contribution lies in the provided systematic dual-domain-based defense framework, which covers three modules named detection, diagnosis, and recovery. Specifically, the CVAE is first introduced for latent- and residual-domain generation. Subsequently, a composite and hierarchical BN detector is proposed to conduct the adversary detection through feature validation and output justification. Afterwards, a diagnosis strategy has been constructed for residual domain and different attacks can be evaluated in the unified framework. Finally, a two-step recovery mechanism is established on the CVAE that can effectively restore the feature representations and the network predictions from various adversaries. The feasibility of the entire defense diagram has been extensively demonstrated on three real-world recognition problems.

This article proposes a modeling method for the fully closed-loop system of hemispherical resonator gyroscope (HRG) under force-to-rebalance mode. A fully closed-loop system consists of two parts: driving loop and detecting loop. First, the dynamic equations of the driving loop are derived to obtain the vibration differential equations of a hemispherical resonator driven by the electrostatic forces, to further obtain accurate output models of the wave amplitude and angular rate, which establishes a model of the driving loop. Next, the output model of the wave amplitude is analyzed to clarify the relationship between the amplitude voltage and wave amplitude. Simultaneously, the influences of interference factors on the amplitude voltage and angular rate voltage are analyzed, which can result in the HRG output error. Then, a model of detecting capacitance is built based on the deformation equations of the hemispherical resonator. A model of the detecting loop is established to analyze its performance and precision. Finally, by tests of amplitude voltage and resonant frequency, the accuracy of the models of the driving loop and detecting loop is proven. A model of the fully closed-loop system of the HRG is proposed based on the models of the driving loop and detecting loop. The HRG output errors caused by the resonator’s deformation, detecting error, and nonuniformity of the quality factor are identified and compensated accurately by the proposed model. It is verified by simulations and tests that the proposed model of the fully closed-loop system is effective, which can not only identify the main errors of the HRG accurately but also provide an accurate model for error analysis.

This article investigates the problem of distributed fixed-time attitude consensus control for multiple spacecraft when only a subset of spacecraft has access to a common reference attitude and angular velocity. Two fixed-time sliding-mode observers are proposed to estimate the reference attitude and angular velocity in fixed time, respectively. Based on estimates of the reference attitude and angular velocity, two distributed fixed-time attitude consensus control schemes are proposed under the mild assumption that there exists a path from the virtual leader to any follower and the communication topology among followers is undirected. In the first control scheme, the measurement of angular velocity is required. However, this requirement is removed in the second control scheme by using a fixed-time state observer to estimate the angular velocity of spacecraft in fixed time. Only a subset of spacecraft is required to access a common reference attitude and angular velocity in the two proposed control schemes. The stability and convergence of the resulting closed-loop systems are guaranteed by the Lyapunov approach. Finally, simulation results validate the effectiveness of the proposed control schemes.

Winding fault diagnosis and failure prognosis technique has been developed based on d-q model of brushless synchronous generator (BLSG) considering actual winding insulation degradation curve of ma...

This paper analyzes the motion capability of multiple fixed-wing unmanned aerial vehicle (UAV) formations by taking speed and heading rate constraints of individual UAVs into account. Unlike most of the existing literature investigating multi-UAV formation flying problems, which mainly focus on planner/controller design in order to achieve desired configurations, this paper aims at providing a quantitative description of the UAV formation maneuverability, which can serve as a metric for evaluating formation trajectory plans. Three common formations, namely perfect rigid formation, semiperfect rigid formation, and flexible formation are considered in this paper. Motion capabilities of these formations are analyzed in terms of turning performance. In particular, the feasible set of the formation heading rate is employed as the evaluation metric, based on which the relationships among these three formations are discussed. It has been proven that perfect rigid formation suffers from a rather limited mobility, while flexible formation has the largest feasible set of the formation heading rate in some situations. An interesting finding of this paper is that flexible formation is not necessarily more agile than semiperfect rigid formation with the same formation width, due to the special stall speed constraint of fixed-wing UAVs. Besides rigorous mathematical derivations, simulations have also been performed to further support the theoretical analyses.

This brief investigates the flexible leader-follower formation tracking problem for a group of nonholonomic mobile robots, while most of the formation control related work in the literature focuses on the rigid formation. The flexible formation discussed in this brief is defined in curvilinear coordinates in terms of longitudinal separations between robots along the reference trajectory and lateral deviations with respect to this trajectory. Unlike the previous studies on flexible formation control, this brief is under a more challenging assumption that the global position and orientation measurements are not available. To obtain the relative pose relationships amongst robots, a stereo camera is mounted on each follower. In consideration of the fact that visual observations are noise-corrupted and intermittently available, a particle filter-based relative pose estimation approach is employed to estimate the position and orientation of the leader in the local reference frame of the follower using the polluted and discontinuous information. Also, to form a flexible formation, the leader historical trajectory is reconstructed with respect to the current local frame attached on the follower, based on which a reference point is generated. In addition, this brief considers the situation where robots operate in unknown obstacle environments. To ensure robot safety in such environments, a multiobjective control law is proposed to balance reference tracking and collision avoidance in different situations. Simulation and real-robot experiment have been performed to demonstrate the efficacy of the proposed method. Accepted version

B cell dysfunction due to obesity can be associated with alterations in the levels of micro-RNAs (miRNAs). However, the role of miRNAs in these processes remains elusive. Here, we show that miR-802 is increased in the pancreatic islets of obese mouse models and demonstrate that inducible transgenic overexpression of miR-802 in mice causes impaired insulin transcription and secretion. We identify Foxo1 as a transcription factor of miR-802 promoting its transcription, and NeuroD1 and Fzd5 as targets of miR-802-dependent silencing. Repression of NeuroD1 in β cell and primary islets impairs insulin transcription and reduction of Fzd5 in β cell, which, in turn, impairs Ca2+ signaling, thereby repressing calcium influx and decreasing insulin secretion. We functionally create a novel network between obesity and β cell dysfunction via miR-802 regulation. Elucidation of the impact of obesity on microRNA expression can broaden our understanding of pathophysiological development of diabetes., Obesity predisposes to type 2 diabetes, but the mechanisms of obesity-associated β cell dysfunction are incompletely understood. Here the authors report that obesity increases the levels of miR-802, which impairs insulin transcription and secretion by targeting NeuroD1 and Fzd5.

Operating multiple robots in an unstructured environment is challenging due to its high complexity and uncertainty. In such applications, the integration of individual maps generated by heterogeneous sensors is a critical problem, especially the fusion of sparse and dense maps. This paper proposes a general multi-level probabilistic framework to address the integrated map fusion problem, which is independent of sensor type and SLAM algorithm employed. The key novelty of this paper is the mathematical formulation of the overall map fusion problem and the derivation of its probabilistic decomposition. The framework provides a theoretical basis for computing the relative transformations amongst robots and merging probabilistic map information. Since the maps generated by heterogeneous sensors have different physical properties, an expectation-maximization-based map-matching algorithm is proposed which automatically determines the number of voxels to be associated. Then, a time-sequential map merging strategy is developed to generate a globally consistent map. The proposed approach is evaluated in various environments with heterogeneous sensors, which demonstrates its accuracy and versatility in 3-D multirobot map fusion missions. Accepted version

Traffic flow prediction is an essential task in the intelligent transportation system (ITS). However, extracting spatial and temporal features from complex traffic data is a critical challenge. Some deep learning-based methods have been applied to perform the traffic flow prediction problem, including convolutional neural network and recurrent network. Recently, the graph-based network has received a lot of attention from researchers, which could model the complex road network as a graph structure. With the rapid growth of graph neural network, it has become a state-of-the-art technique to perform the traffic prediction tasks. However, these deep learning-based traffic prediction approaches require a huge amount of traffic data, while many road segments suffer from the lack of historical traffic data. Meanwhile, some traffic data are missing due to unexpected issues such as malfunction of detectors. The severe data paucity problems will greatly degrade the performance of prediction. To address this challenge, this paper proposes a domain-adversarial-based neural network for traffic flow prediction. Contrast experiments have been conducted on several open-source traffic datasets, which have well demonstrated that the domain adaptation techniques could transfer the learned features from a source road network to a target network with data paucity problem. It also illustrates the effectiveness of our proposed method and shows the importance of domain adaptation method in traffic prediction problem.