In remote sensing image classification, open-set recognition (OSR) poses a significant challenge, aiming to accurately classify known categories while effectively rejecting unknown class samples or identifying potential novel categories. Although existing methods have made strides in recognizing known classes, they exhibit notable limitations in handling unknown class samples. This letter introduces an OSR model for ship detection, termed masked autoencoder (MAE)-based OSR (MOSR), which leverages the robust representation learning capabilities of the MAE. MOSR not only sustains high accuracy in the recognition of known classes but also markedly enhances the performance in the identification of unknown class samples. Comprehensive experiments on the custom RSHIP-137 remote sensing dataset validate the efficacy and superiority of the MOSR model. Compared with the state-of-the-art (SOTA) adversarial reciprocal point learning (ARPL) method, MOSR shows substantial improvements in both known class recognition accuracy and the area under the receiver operating characteristic curve (AUROC) for unknown class recognition for ship detection. This study presents a novel solution for OSR in remote sensing ship detection and offers valuable insights for future research.

Navigating in a crowded social environment without collisions or freezing is a crucial and challenging task. Recent studies have demonstrated considerable success using Deep Reinforcement Learning for social robot navigation. However, the lack of safety guarantees remains a prevalent problem among these methods. To address this limitation, we present a comprehensive safety framework that builds upon a novel multi-agent environment single-agent decision-making formulation and the strengths of Safe Reinforcement Learning (Safe RL) methods. First, considering the uncertainty of pedestrians and the inaccuracy of trajectory predictions, we explicitly embed confidence-weighted uncertainty and risk measurement in the interaction graph network to induce adaptive policies. Next, we replace the state-wise robust-form unshaped cost with a cumulative robust dynamical distance constraint, which establishes a stronger and essential safety objective while ensuring the invariance of the optimal policy. Then we use reachability-based Safe RL to ensure state-wise safety assurance of the feasible part and the risk minimization of the infeasible part. Simulation and virtual-reality evaluation results demonstrate that our proposed method outperforms the state-of-the-art methods in various metrics, such as success rate and collision energy, achieving the best trade-off between safety and efficiency. Our proposed method yields a policy that exhibits desired collision avoidance behavior in both simulation and real-world experiments.

Establishing the correspondences between newly acquired points and historically accumulated data (i.e., the map) through nearest neighbor search is crucial in numerous robotic applications. However, static tree data structures are inadequate to handle large and dynamically growing maps in real-time. To address this issue, we present the i-Octree, a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, such as point insertion, deletion, and on-tree down-sampling. The i-Octree is built upon a leaf-based octree and has two key features: a local spatially continuous storing strategy that allows for fast access to points while minimizing memory usage, and local on-tree updates that significantly reduce computation time compared to existing static or dynamic tree structures. The experiments show that the i-Octree outperforms contemporary state-of-the-art approaches by achieving, on average, a 19% reduction in runtime on real-world open datasets.

This paper addresses trajectory planning and control for a dual-arm free-floating space robot (FFSR) based on predefinedtime stability. Firstly, a class of general nonsingular terminal sliding mode control strategy is proposed to achieve global predefined-time stability by switching a control parameter in a form of function. Then, a high-precision trajectory planning method is proposed for a FFSR using the proposed predefined-time stability theorem, which can make the end-effector and the base reach the desired pose within a prespecified time, and reduce the adverse effect on planning accuracy resulted from the singularity avoidance algorithm and the obstacle avoidance one. Subsequently, a trajectory tracking control strategy is proposed for a FFSR employing the proposed general nonsingular predefined-time terminal sliding mode, and the tracking errors can converge within a predefined time to an arbitrarily small neighborhood of zero in the presence of persistent disturbances. The proposed trajectory planning method has the advantage of pose feedback, so can be combined with the trajectory tracking controller in the control process to further enhance the control performance for the FFSR. Simulation results validate the effectiveness of the proposed methodologies.

In this paper, we study the fast coverage problem of 3D irregular terrain surfaces with a hierarchical UAV swarm. We first build a 3D model of a random irregular terrain and project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we adopt the traditional coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. The numerical results show that the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithm.

The hip assistance robotic exoskeleton has been demonstrated as an effective device to assist elderly and disabled people with gait disorders. The assistance efficiency of these devices, however, is less optimized because the parameters in the active impedance control are manually designated. This paper presented a novel assistance control scheme to address the sub-optimal issue. This study poses that the assistance efficiency can be maximized by modifying the mechanical impedance to resonate with the muscle driving force, in which the human-exoskeleton coupling system is approximated with a second-order dynamical system. Based on this, the exoskeleton virtual stiffness is adaptively tuned to make the system intrinsic frequency align with the intended swing frequency. The proposed assistance control scheme demonstrated an increased assistance efficiency than the conventional active impedance control in a simulated study. Experiments that were managed on a newly custom-made hip assistance robotic exoskeleton also demonstrated strong evidence of improved gait kinematics with decreased muscle-skeleton efforts.

Unmanned aerial vehicle (UAV) technology is recognized as a promising solution to area coverage problems (ACPs) and has been extensively studied recently. In this paper, we study the 3D irregular terrain surface coverage problem with a hierarchical UAV swarm. We first build the 3D model of a random irregular terrain and propose a geometric way to project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we design a coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. Moreover, an observation history model based on convolutional neural networks (CNNs) and the mean embedding method is integrated into SDQN to address the communication limitation problems of LUAVs. The numerical results show that FUAVs can cover the entire area of each patch with little redundancies, and the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithms.

Limited by the operation time window and working space, space teleoperation tasks need to be completed within an expected time while ensuring that the end effector meets the physical constraints. Meanwhile, the interaction with unknown environments would cause uncertainty in the closed-loop system, which brings great challenges to the control design. To solve the above problems, the control performance issue for a class of space teleoperation systems subject to multiple constraints and interaction uncertainties is investigated in this article. The force interaction with the human operator/space environment is represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems, where the uncertain equivalent mass and damping parameters can be effectively described and captured by IT2 membership functions. In order to reduce the communication burden and satisfy the constraints of settling time, transient-state performance and operating space, a time-varying threshold event-triggered control scheme together with exponential-type Lyapunov function is developed for the first time. We show that, with the proposed controller, the synchronization tracking errors are guaranteed to converge to a user-defined residual set within preassigned settling time, and never exceed the prescribed range despite unknown control direction and actuator faults, which solves the long-standing constraint issue with more flexibility due to the fact that the related constraints can be arbitrarily specific within the physically available range. Moreover, the convergence set is only dependent on fewer user-defined parameters rather than approximation errors, which provides an effective analysis technique to deal with the difficulty that the convergence accuracy is difficult to calculate quantitatively in the presence of unknown disturbance. Detailed simulation results are provided to show the effectiveness and merit of the proposed control strategy.

This Innovate Practice Full Paper presents the past process of the full-time Master of Engineering (M. Eng.) education in China, which is designed to meet the requirements of the China's innovation of science and technologies. Under the current trend of the economic globalization and the internationalization of higher education, China's higher engineering education (HEE) must carry out certain reforms to adapt to the new changes and development of the country. In China's HEE, M. Eng. program can be divided into two categories: in-service M. Eng. program and full-time M. Eng. program. The graduate students of the former program come from the employees of enterprises and study in their spare time, while the graduate students of the latter program mainly come from the fresh graduates of universities and they spend their full time for study. Normally they both spend 2 to 3 years to finish their master programs. This paper mainly discusses the current situation of full-time M. Eng. education, and analyzes the corresponding difficulties from the following four aspects: insufficient clarity in the training goal, incompleted Dual Tutor System, unsatisfactory enterprise practice, and insufficient interaction between university and enterprise. Therefore, full-time M. Eng. education still faces some immature situation and has higher demand for the innovation. This paper takes the big data full-time Master of Engineering (BDFM) training as a case, and introduces a new training and practice mode in a Chinese university, including project management, establishment of Practice Education Base (PEB), course construction, design of mechanism of PEB, as well as the establishment of Dual Tutor Matching Mechanism (DTMM). Considering the internationalization of HEE under China's national conditions, we show how to speed up the educational reform of M. Eng., how to implement educational innovation, how to promote the engineering education to better adapt to the needs of economic and social development, and how to play the exemplary role of this new training mode. We hope our study could contribute to the sustainable development of HEE over the world.

This paper is concerned with interval type-2 (IT2) fuzzy control design for a class of nonlinear space teleoperation systems with external disturbances and time-varying delays. IT2 fuzzy model based (FMB) control design with exponential-type Barrier Lyapunov function (EBLF) is presented to address state constraints, communication burden from ground stations to satellites (space-robot), and uncertain human/environment interaction parameters in a unified event-triggered control structure. We show that, with the proposed adaptive event-triggered control scheme, the exponential convergence performance of the synchronization tracking errors is guaranteed, while the prescribed constraint requirement is satisfied. Simulation results are provided to validate the effectiveness of the proposed controller.

In space mission, the tracking accuracy of robot will be affected by many factors. Fortunately, some errors can be eliminated by trajectory planning without the need for additional fuel, hence, in this paper, a nonsingular error-free trajectory planning method for a dual-arm free-floating space robot(FFSR) will is studied with the large initial measurement errors and non-zero dynamic of base. Firstly, the pose error kinematic equation is deduced by the desired pose command and related kinematics. Then, a predefined-time stability function is used as the pose-feedback control law to converge the error to zero, and the convergence time is bounded. In the end, the motion trajectory of balance arm will be planned by the momentum conservation equations of the system to offset the dynamic coupling. The predefined-time stability theorem will significantly improve the anti-disturbance performance of the traditional pose-feedback based trajectory planning method, and the error can be eliminated within any required task time. Simulation results validate the effectiveness of the proposed methodology.

To obtain synchronized gait assistance, this paper presents a new delayless adaptive dual-oscillator (ADO) scheme to address the inherent delay issue. In the ADO structure, a new oscillator is coupled with the primitive one but the phase is adaptively feed-forward compensated. It's remarkable that the compensated phase is determined by the proposed extended phase lag observer, in which both the phase lag and phase leading can be properly estimated and eliminated in the steady and non-steady gait. Moreover, a unified exoskeleton control scheme based on ADO is further proposed to improve the gait segmentation, velocity/acceleration estimation, intention estimation, and assistance generation performances, which further enhances the assistance synergy and reduces the safety risks. Experimental results demonstrate better alignment assistance and consequently reduced muscle efforts with ADO-based assistance control.

Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end, a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme, a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover, a fractional power sliding mode control law is designed to realize fixed-time convergence, where the convergence time is irrelevant to initial states or external disturbance, and depends only on the chosen parameters. To further enhance observer robustness, an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.

In this article, we present an adaptive prescribed-time control method for a class of nonlinear telerobotic systems with actuator faults and position error constraints. Extended from prescribed-time stability, practically prescribed-time stability (PPTS) is proposed for the first time aiming at stability analysis and control synthesis of nonlinear systems with disturbance and uncertainty. We show that, under the control scheme in the framework of PPTS, the system states are guaranteed to converge to a user-defined set (physically realizable) within user-defined settling time (physically realizable). Based on PPTS, an adaptive fault-tolerant controller is developed by integrating a novel exponential-type barrier Lyapunov function. Rigorous stability analysis based on back-stepping approach proves that, under the proposed control strategy, synchronization errors converge to a user-defined residual-set within predefined settling time and never exceed the prescribed range. Universal performance indexes, including the settling time, residual-set, accuracy, and overshoot, can be user-defined and only dependent on fewer user-defined parameters. Simulation results illustrate the effectiveness of the developed control scheme.

In this study, a human-chair model was developed as the basis for a wearable chair design. A prototype chair, HUST-EC, was fabricated and evaluated. Employing the optimization under an inner point penalty function, an optimized simulation of the operating mode with the lowest chair height was implemented. The solid models were established by using the finite element analysis program embedded in Solidworks, which revealed that the support from the designed chair was steady to the user. An electromyography (EMG) test platform has been developed, consisting of four EMG sensors, a MATLAB-based acquisition software, and a loaded vest. Four healthy subjects participated in the evaluation experiment, in which EMGs were collected from the muscle groups of rectus femoris, biceps femoris, vastus medialis, and vastus lateralis under different loads and chair angles. The experimental data demonstrate that (1) the HUST-EC can greatly reduce muscle activation at a variety of loads and bending angles; (2) under the same load, the muscle activation decreases slightly with an increased bending angle; and (3) at the same bending angle, muscle activation increases slightly with an increased load. The results show that the designed chair can effectively reduce the physical burden in workers and may improve work efficiency.

The ability to handle outliers is essential for performing the perspective- n-point (P nP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P nP solution named R1PP nP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P nP algorithm, which serves as the core of R1PP nP, for solving the P nP problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP nP is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP nP is among the most accurate and fast P nP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP nP, which is the state-of-the-art P nP algorithm with an explicit outliers-handling mechanism, R1PP nP is slower but does not suffer from the percentage of outliers limitation as REPP nP.

Patch-based image registration is a challenging problem in visual geometry, the crucial component of which is the selection of an appropriate similarity measure. The similarity measure participates in the objective calculation of the pose optimization, which determines the optimization convergence performance. In this paper, we propose learning a similarity metric of patches from reference and target images such that the pairwise patches with a small projection error receive high similarity scores. To achieve this objective, we designed and trained the classification, regression, and rank networks separately based on self-collected data sets. The network can directly output the projection error according to the patches, which is sensitive to the deviation of the pose transformation. We also designed evaluation criteria and validated the superior performance of the network's outputs compared with the performance of traditional methods, such as the sum of absolute difference and the sum of squared differences.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.

Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years, and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game. GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. In this paper, we introduce and investigate the use of GAN for novelty detection. In training, GAN learns from ordinary data. Then, using previously unknown data, the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns. The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman (TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling’s T 2 and squared prediction error statistics.

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

Monocular simultaneous localization and mapping (mono-SLAM) is a key component of autonomous robot visual navigation. Recently, the structure from motion (SfM) approach has become an attractive means of implementing mono-SLAM because of its high accuracy; although, in this application, the SfM approach must be operable in real time and robust to outliers. However, because of strong nonlinearity, conventional SfM methods, such as bundle adjustment, must consider multiple initial values to obtain the globally optimal result, which is time consuming. In this paper, a novel iterative SfM algorithm based on the object-space objective function is proposed. To improve the method's robustness to outliers and to incorporate the information obtained from other types of sensors, an approach to closely integrate the proposed SfM algorithm with extended Kalman filter-based SLAM is proposed. Experimental results using both synthesized and real data are consistent with our theory, and verify that the main advantage of the proposed SfM algorithm is its good convergence. The algorithm is therefore particularly appropriate for realizing mono-SLAM, because when rotations can be obtained approximately using gyroscope information, the algorithm is globally convergent from any initial value.

In this paper, we design transmit beamforming for a full duplex base station (FD-BS) considering both self-interference mitigation and physical-layer security. The proposed design is formulated as minimizing the power consumption of FD-BS under different signal-to-interference-and-noise-ratio (SINR) constraints. Semidefinite relaxation (SDR) is used to convert the initial nonconvex optimization to be a convex semidefinite programming (SDP) problem. Then the optimality of SDR is strictly proved by showing the existence of the rank-one optimal solutions. To reduce the computational complexity, we develop zero forcing beamforming-based suboptimal algorithms, where the solutions can be obtained using golden search and closed-form solutions can be derived in each step. Simulation results are then provided to verify the efficiency of the proposed algorithms.

In this paper, we study a challenging problem of inferring individuals' role and statuses in a professional social network, which is of central importance in workforce optimization and human capital management. Realizing the natural setting of social nodes associated with dual view information, i.e., The local node characteristics and the global network influence, we present a novel model that explores graph regularization techniques and integrates such information to achieve improved prediction performance. In particular, our prediction model is built upon the graph transductive learning framework that encodes an uncertainty regularization term in the conventional empirical risk minimization principle. Through taking advantage of the information from both the local profile and the global network characteristics, the final inference of the role or statues achieves minimum an empirical loss on the labeled set, as well as a minimum uncertainty on the unlabeled social nodes. We perform extensive empirical study using real-world data and compare with representative peer approaches. The experimental results on three real social network data sets show that the proposed model greatly outperforms a number of baseline models and is able to effectively infer in a wide range of scenarios.

Detecting human behaviors in vast amounts of video is a challenging task in a variety of real-world applications. Thus an interactive tool designed to support this task with human in the loop is of significance in various domains including public safety and security. In this paper, we design and develop an interactive visual analytics system, RiskWheel, that enables effective analysis of detection results and utilization of user feedback to improve surveillance event detection. In particular, we propose 1) an interactive approach to visualize data with temporal relations and 2) a novel risk ranking method to differentiate detection results and present more informative ones to the user for better interaction. In our experiments, we demonstrate RiskWheel through a case study on TRECVID Surveillance Event Detection (SED) task [1]. The experimental results quantitatively show that RiskWheel outperforms multiple baselines, demonstrating the power of the risk ranking technique.

Pose estimation from points with unknown correspondences currently is still a difficult problem in the field of computer vision. To solve this problem, the SoftSI algorithm is proposed, which can simultaneously obtain pose and correspondences. The SoftSI algorithm is based on the combination of the proposed PnP algorithm (the SI algorithm) and two singular value decomposition (SVD)-based shape description theorems. Other main contributions of this paper are: 1) two SVD-based shape description theorems are proposed; 2) by analyzing the calculation process of the SI algorithm, the method to avoid pose ambiguity is proposed; and 3) an acceleration method to quickly eliminate bad initial values for the SoftSI algorithm is proposed. The simulation results show that the SI algorithm is accurate while the SoftSI algorithm is fast, robust to noise, and has large convergence radius.

For an electrical six-degree-of-freedom Stewart platform, it is difficult to compute the equivalent inertia of each motor in real time, as the inertia is time-varying. In this study, an analysis using Kane’s equation is undertaken of the driven torque of the movements of motor systems (including motor friction, movements of motor systems along with the actuators, rotation around axis of rotors and snails), as well as driven torque of the platform and actuators. The electromagnetic torque was calculated according to vector-controlled permanent magnet synchronous motor (PMSM) dynamics. By equalizing the driven torque and electromagnetic torque, a model was established. This method, taking into consideration the influence of counter electromotive force (EMF) and motor friction, could be applied to the real-time dynamic control of the platform, through which the calculation of the time-varying equivalent inertia is avoided. Finally, simulations with typically desired trajectory inputs are presented and the performance of the Stewart platform is determined. With this approach, the multi-body dynamics of the electrical Stewart platform is better understood.

This paper reviews the mixed H2/Hinfinfiltering problem for linear time-invariant discrete-time systems with polytopic uncertainty. By introducing some additional free variables, a parameter dependent Lyapunov stability condition other than the quadratic stability condition is obtained in a polytopic domain, and then a much less conservative filter design approach is presented via solving some LMIs formulated with correspondence to polytopic vertices.

This paper analyzes and designs a Testing and Communication System (TCS), which is used for the simulation of controlling and communicating for the space robot arm. Arming at enhancing the reliability of the operation of the space robot arm, a new Hot Double redundant strategy is adopted during the designing of TCS. The new Hot Double Redundant way is utilized to back up CAN BUS, which is the medium of data transmission and reception. The new network structure based on CAN BUS is validated on a nine nodes experiment system. The simulation results demonstrate the effectiveness of the redundancy strategy, and also the reliability and fault-tolerant ability of the TCS is testified by experiments.

This paper presents a knowledge-based person-centric human-robot interaction system using facial and hand gestures. In the proposed method, face detection and person identification are first made. With the knowledge of the known user face and hand poses are then classified from the later image frame by the subspace method and the gestures are finally recognized. The rules for interpreting the gestures are selected according to each specific user recognized by the facial image. The user's name and gesture commands are sent to the robot through a Software Platform for Agent and Knowledge Management (SPAK) to implement person-centric human-robot interaction. The effectiveness of this method has been demonstrated by interacting with a humanoid robot Robovie.

This paper proposes a knowledge model-based coordinator robot, in order to realize coordination of tasks by robot instead of human being according to human requests in a symbiotic autonomous human-robot system. This coordinator robot is constructed based on a knowledge model of coordination. In this model, a physical domain world on coordination is modeled by a description world, including interpreter for the human request domain, coordination interface for the problem domain, coordination policy for the solution domain, etc. In this paper, a coordinator robot is implemented by means of a software platform and distributed intelligent agents. The experimental work with using actual robots demonstrates the effectiveness of the proposed method.