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This paper addresses the event-triggered output feedback control problem of steer-by-wire (SbW) systems subject to uncertain nonlinearity and time-varying disturbance. First, to solve the uncertainty and remove unnecessary sensors, an interval 2 fuzzy logic system (IT2 FLS) and an adaptive state observer are proposed to estimate the uncertain nonlinearity and unavailable states of SbW systems. Then, an event-triggered output feedback control method is constructed for SbW systems to achieve the prespecified tracking performance. Much significantly, the jumping phenomenon of the control input caused by event-triggering communication can be eliminated by the proposed event-triggered control systems. Furthermore, theoretical analysis shows that the tracking error can converge to the preset neighborhood of origin within finite time, while the Zeno behavior can be avoided. Finally, simulations and vehicle experiments are presented to verify the validity of the proposed methods.

This paper puts forward an unprecedented avoidance-striking-arrival problem aiming to address the need for tank's uncertain mechanical systems on the intelligent battlefield. The associated system uncertainties (possibly rapid) are time-varying but bounded (possibly unknown). The goal is to design a controller that enables the tank to aim at and attack the enemy tank while keeping itself (out of the enemy fire zone). The tank maintains this condition until reaching the predefined region. In this paper, an approximate constraint following control method is adopted to solve this problem, and the original constraints are creatively divided into two categories: the avoidance - tracking constraint and the striking - arrival constraint. An adaptive robust control method is proposed and consequently verified through simulation experiments. It is proved that the system fully obeys the avoidance - tracking - constraint and strictly obeys the striking - arrival constraint under the control input τ . Besides, the control of the tank vehicle running system and tank gun bidirectional stabilization system are unified to deal with the control signal delay caused by complex uncertainties on the battlefield. Overall, this paper reduced the delay of signal transmission in the system while solved the avoidance - striking - arrival problem.

The tracking control of a wastewater treatment process (WWTP) is considered. The process is highly nonlinear, with strong coupling, difficult to model mathematically, and the operation is subject to unknown disturbances. We address this multivariable tracking control problem by applying the direct heuristic dynamic programming (dHDP)-based reinforcement learning control. The control goal is to track a desired reference of the dissolved oxygen (DO) concentration of the 5th aerobic zone ( $S_{O5}$ ) and nitrate concentration of the 2nd anoxic zone ( $S_{NO2}$ ) by manipulating the oxygen transfer coefficient of the 5th aerobic zone ( $K_{L}a_{5}$ ) and internal recycle flow rate ( $Q_{a}$ ). The dHDP aims at achieving a minimal accumulated WWTP tracking error while dealing with strong coupling between the $S_{O5}$ and $S_{NO2}$ and eliminating unknown disturbances in the process. The proposed dHDP approach devises an optimal control strategy entirely driven by WWTP process data as an online learning control method. We have conducted extensive and systematic simulations based on the well-known BSM1 platform of the WWTP controlled by dHDP to compare and contrast performances with other methods.

This article addresses the three-dimensional (3-D) coordinated control problem of directed networked aircraft-like vehicles, that is to track a set of given orbits on a sphere and achieve a lateral formation flight. Different from the case of Newton particles, a nonholonomic dynamics with unknown disturbances is considered. A novel method to decouple the spherical orbit tracking subsystem and the lateral formation flying subsystem is proposed. By overlooking the control of the vehicle's surge velocity, a nonsmooth spherical orbit tracking algorithm is designed by backstepping. Without considering the spherical orbit tracking errors and using any global information of topologies, a distributed, nonsmooth formation protocol is designed. The input-to-state stability (ISS) theory is used to analyze the converge property of the interconnected system consisting of these two subsystems. Simulation results are given to verify the theoretical analysis.

The conventional maximum power point tracking (MPPT) controllers are unable to track the global maximum power point (GMPP) during partial shading conditions (PSCs). As a result, many optimization strategies have been used to track the GMPP. Optimization approaches, on the other hand, are iterative methods that cannot distinguish uniform shading conditions (USCs) from PSCs. In this paper, a new partial shading detection approach has been hybridized with the new modified rat swarm optimization algorithm (MRSO). It successfully distinguishes USCs from PSCs in two samples and triggers the MRSO only when the PSC is detected. Consequently, it avoids the excessive global peak search during the USCs, which improves the tracking time for USCs without an unnecessary scan of the entire P–V curve. Furthermore, the proposed MRSO, is simple to implement with simple tuning parameters and fast tracking speed for PSCs. The proposed method has been experimentally validated on a boost converter with a sampling time of 0.05 s. The experimental results showed the successful implementation of the proposed method with an average tracking time for uniform and PSCs of 0.42 s with an average steady-state efficiency of 99.8%. Subsequently, a comparison with state-of-the-art methods in this domain has been provided to assess the efficacy of the proposed method.

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean square and almost sure convergence are provided. Numerical simulations on a 4 DoF robot arm are presented to illustrate the effectiveness of the proposed scheme.

In this paper, for the line-of-sight (LOS) tracking problem of a tractor-trailer vehicle system, we propose a novel adaptive constrained tracking control algorithm for the trailer to track a desired trajectory, while satisfying multiple performance and feasibility constraint requirements during the operation. To deal with these constraint requirements, both a universal barrier function approach and a novel state transformation scheme are incorporated to deal with constraints of different nature. An adaptive control structure is introduced to deal with system parameter uncertainties and external disturbances. We show that for the LOS distance and angle tracking errors, exponential convergence into small neighbourhoods of equilibrium can be guaranteed, with the convergence rate depending on the control input and adaptive gains. In the end, a simulation example and an experimental study further demonstrate the efficacy of the proposed algorithm.

In this article, quadrotor unmanned aerial vehicles (QUAVs) are organized as a two-layer structure, where the first layer is leader QUAVs and the second layer is follower QUAVs. In this structure, only the leader QUAVs can receive the desired tracking information of position and attitude. Although the followers cannot obtain the given tracking information directly, they can obtain the corresponding information from leaders and other followers through the communication network based on the graph theory. In terms of this case, a distributed formation-containment (FC) control method is proposed to handle the related flight problems. We aim to develop a formation control for the leader QUAVs and a containment control for the follower QUAVs with the graph theory. Furthermore, a neural network (NN) technique is utilized to cope with the uncertainty of each QUAV. In order to guarantee good flight performance when tracking, the finite-time stability theorem is introduced into the control design to make each QUAV achieve satisfactory tacking performance in finite time. Finally, numerical simulations are conducted in the platform of two-layer 16 QUAVs to validate the feasibility and effectiveness of the proposed NN-based finite-time FC control.

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://github.com/JustMeZXX/Intelligent-Night-Time- Traffic-Surveillance

In this work, we propose a novel framework to address the formation control problem for a class of multirobot systems with two types of constraints, namely the performance constraints and the feasibility constraints. For the performance constraints, we consider the constraint requirements on the distance tracking errors between the real and the desired trajectories for each robot, so that to ensure precise tracking of the robot without deviating too much from its desired trajectory, as well as the constraints on the interrobot distance, so that to ensure the safe operation of the team. For the feasibility constraints, we consider the constraints on the heading angle, so that the controllers designed in the brief are feasible. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address systems with different types of constraints in a unified controller architecture. Through rigorous analysis, exponential convergence rate can be guaranteed on the distance tracking errors, while all constraints are satisfied during the operation. A simulation example and an experiment using three AmigoBot mobile robots further demonstrate the efficacy of the proposed control framework.

This article focuses on the distributed cooperative compound tracking issue of the vehicular platoon. First, a definition, called compound tracking control, is proposed, which means that the practical finite-time stability and asymptotical convergence can be simultaneously satisfied. Then, a modified performance function, named finite-time performance function, is designed, which possesses the faster convergence rate compared to the existing ones. Moreover, the adaptive neural network (NN), prescribed performance technique, and backstepping method are utilized to design a distributed cooperative regulation protocol. It is worth noting that the convergence time of the proposed algorithm does not depend on the initial values and design parameters. Finally, simulation experiments are given to further verify the effectiveness of the presented theoretical findings.

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

For high-resolution imaging implementations, the spacecraft attitude tracking control accuracy is crucial to determining the imaging quality. This investigation addresses the attitude tracking issue of imaging spacecraft subject to system uncertainties (unavailable inertia tensor, unexpected disturbances, and actuator faults). An adaptive sliding mode control (SMC) strategy is proposed to guarantee practical fixed-time closed-loop stability even in the presence of system uncertainties. Unlike existing methodologies, the sliding mode surface is developed to satisfy a novel sufficient condition of fixed-time stability. The sliding manifold design also circumvents the unwinding phenomenon arising in quaternion representations. Particularly, this controller is developed to generate a smooth control profile by using a new parameter update law. Rigorous Lyapunov analyses are further employed to ensure the fixed-time closed-loop stability irrespective of the system initial states. Finally, numerical examples are performed to demonstrate the feasibility and highlight the inherent features of the derived control law.

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods.

For leader-following networked systems with the topology switching and the aperiodic silence, limited-energy output formation tracking problems are investigated. Firstly, a new output formation tracking control protocol is proposed, which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents, respectively, and the aperiodic silence, the topology switching and the energy constraint index is introduced properly. Then, a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader, and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented, which are extended into networked systems without the aperiodic silence. Especially, a partition checking algorithm is presented to check limited-energy output formation tracking design criteria. Finally, a numerical example is illustrated to demonstrate the validness of theoretical results.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this paper proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average F_{measure}. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR.

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method.

In order to improve trajectory tracking accuracy, a reinforcement learning method was employed to address the trajectory tracking task in autonomous driving. There are many conveniences and advantages in theories, methods and tools utilizing reinforcement learning to solve trajectory tracking control problems. To teach an intelligent agent effective driving skills, the structured road information in the urban environment was extracted to generate an accurate reference trajectory. Then real-world scenarios were modeled to build a simulation environment for training. To effectively train the intelligent driving agent, Imitation Learning was firstly employed to teach the agent primary driving skills. Afterwards, Reinforcement Learning was adopted to optimize the agent's driving policy. After the intelligent driving agent was well trained in the simulator, the tracking experiments were conducted in the simulator and the real-world scenarios. The proposed method was compared with base-line methods of geometric tracking, optimization-based tracking and learning-based tracking. The experimental results demonstrated that Reinforcement-Tracking can achieve accurate trajectory tracking performance and even exceed the accuracy of most baseline methods.

Microswimmer and miniswimmer toward precision-targeted medicine have attracted extensive attention recently. We have developed an autonomous manipulation approach for magnetic-driven helical miniswimmer at low Reynolds number in the horizontal plane (H-plane). Different from our previous work which just makes the barycenter of miniswimmer on the reference path as well as takes the swimming direction not into consideration in planar path following, our control policy in this article can make the miniswimmer to follow the reference path and, at the same time, its swimming direction is also along the reference path. A robust tracking method is employed to locate the helical miniswimmer in real time. Due to different external disturbances, an angle compensating model in the global coordinate frame is developed by radial basis function (RBF) networks trained by backpropagation algorithms, which is used to express the swimming model of the helical miniswimmer facing the gravity and lateral disturbances. A discrete-time optimal controller is formulated based on the linear-quadratic feedback control. Simulations and experiments are conducted to quantitatively validate the autonomous manipulation, and the results show the control performance with submillimeter accuracy in the H-plane.

Pedestrians represent acutely complex and agile targets whose accident prevention has the highest priority concerning highly automated vehicles in urban traffic. Consequently, detection and tracking methods are necessary to determine motion behavior quickly and with high precision. With advancing microelectronics, powerful radar sensors and novel antenna structures are available for pedestrian detection systems, which can utilize unique features such as micro-Doppler signatures in several dimensions and high resolution. This paper presents a micro-Doppler based leg tracking framework that enables behavioral indications in a few sensor cycles. An adaptive motion model representing the kinematic locomotion of a human is derived to propagate the foot motion. Joint probabilistic data association assigns the detections to the respective leg and allows its temporal filtering of the position, micro-Doppler velocity, and kinematic parameters using an extended Kalman filter yielding real-time implementation capability. The presented approach provides the entire signal chain from raw radar data processing to extended multi-object state estimation based on highly relevant safety-critical motion maneuvers measured with radar parameterization corresponding to current serial short-range sensors.

This study addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. Firstly, new finite-time performance functions are developed to construct a constraint funnel which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

In performance tests, replacing humans with robotic drivers has many advantages, such as high efficiency and high security. To realize the vehicle speed tracking control with a robotic driver, this article proposes a novel deep reinforcement learning (DRL) approach based on deep deterministic policy gradient (DDPG). Specifically, the design of the approach includes state space, action space, reward function, and control algorithm. Then, to shorten the training time, the proposed approach utilizes the basic fundamental relationship between vehicle speed and pedal opening to intervene in network exploration. Furthermore, to solve speed fluctuations in low-speed sections, the replay buffer is optimized by adding weighted training samples. Experiments are conducted on fifteen cars, and results show that the proposed algorithm can effectively control the vehicle speed. Generally, it only needs three or four episodes of training to meet the requirements. Compared with the Segment-PID method, the proposed method has a smoother speed and fewer overbound times.

Time-varying formation (TVF) and trajectory tracking $H_{∞}$ control problem of multiagent systems (MASs) subject to communication delays and external disturbances under the directed communication topology is studied. This article's objective is for all agents to attain the desired TVF and track the pregiven formation center trajectory simultaneously. First, a distributed TVF and trajectory tracking control protocol employing neighborhood interaction information is developed in the presence of communication delays. Second, since the Laplacian matrix of a graph can be decomposed into the product of two specific matrices, the TVF and trajectory tracking $H_{∞}$ control problem is converted into the lower dimension asymptotic stability problem of a closed-loop system by applying an appropriate variable conversion. Third, a Lyapunov-Krasovskii functional is constructed to analyze the stability of MASs. Sufficient conditions are obtained in the form of linear matrix inequalities (LMIs) to ensure the completion of the TVF and formation center trajectory tracking of MASs. In the meantime, the maximum allowable communication delay can be calculated by the LMIs. Finally, the results of numerical simulations are presented to verify the validity of the approach this article proposes.

Radar and camera information fusion sensing methods are used to solve the inherent shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the main hardware and camera as the auxiliary hardware framework. At the same time, the Mahalanobis distance is used to match the observed values of the target sequence. Data fusion based on the joint probability function method. Moreover, the algorithm was tested using actual sensor data collected from a vehicle, performing real-time environment perception. The test results show that radar and camera fusion algorithms perform better than single sensor environmental perception in severe weather, which can effectively reduce the missed detection rate of autonomous vehicle environment perception in severe weather. The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a $\bm {12\,\times 10\,\times \text{10-mm}}$ microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

To shorten the scanning time by focusing on the local scanning for such specimens as cells, this paper proposes an advanced scanning strategy based on autonomous exploration, so as to detect the specimen in real time and achieve fast imaging for an atomic force microscopy (AFM). More specifically, fast raster scanning is first performed to locate the initial boundary point of the specimen. On this basis, a boundary tracking algorithm is proposed to construct the internal boundary of the specimen online through the autonomous exploration of the probe. Afterwards, the boundary is expanded according to the internal boundary tracking direction, based on which a convex hull of the specimen is further constructed for the local scanning. Furthermore, the local slow scanning is performed for the specimen according to the generated scanning trajectory. Subsequently, unscanned areas in the sample can also be scanned by this autonomous method. Experimental results verify that the proposed method can achieve fast scanning while ensuring high-quality imaging.

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

Neurodegenerative disorder diseases, such as Parkinson’s Disease, are progressive, and their motor symptoms develop slowly. Assessing the motor functions of PD patients relies on the physician’s experience and subjective judgment. However, the disease’s actual condition may not fully present during the clinical examination due to the motor fluctuation or symptom variation in a day. This paper proposed a wireless detection method that can enable long-term monitoring and quantification of walking, one of the challenging metrics to be evaluated in clinics. We utilized the Channel State Information from two Wi-Fi links to construct a two-dimensional coordinate system in space to locate, track, and capture the gait information of a walking subject, including walking distance, step length, and cadence. The results showed that the estimation errors were 0.02 – 0.16 m (17.84% – 38.48%) for step length, 0.01 – 0.16 Hz (1% – 11%) for cadence, and less than 8.84 for walking direction when a subject walked along a straight line. The method could also detect walking that constantly changes direction (circular path) with localization errors of 0.31 (CW) and 0.50 m (CCW). Moreover, the proposed method could distinguish two subjects when they simultaneously walked along non-predefined paths. The method can be further developed as a home health monitoring technology to provide long-term and reliable data without personally identifiable information for physicians to make more precise treatments.

In active object tracking, given a visual observation as input, the goal is to lockup the target by autonomously adjusting camera’s position and posture. Previous works on active tracking assume that there is only one object (person) in the environment without distractors. In this work, towards realistic setting, we move forward to a more challenging scenario, where the tracker moves freely in 3D space like unmanned aerial vehicles (UAV) to track a person in various complex scenes with multiple distractors. To this end, we propose a novel end-to-end anti-distractor active object tracking framework by introducing multiple attention modules. On one hand, we take the target template to learn an embedding as channel-wise attention for current observation to distinguish the target from the distractors. On the other hand, temporal attention is introduced to fuse the observation history to extract a feature representation, which is then fed into a reinforcement learning network to output the action of the tracker. To evaluate our method, we build several multi-object 3D environments in Unreal Engine and extensive experiments demonstrate the effectiveness of our approach.

This paper presents output feedback control for longitudinal dynamics of fixed-wing aircraft using singular perturbation techniques. The longitudinal dynamic model is separated into two timescales. A composite feedback control law based on sliding mode control (SMC) is designed for slow and fast subsystems to track the aerodynamic velocity and flight path angle references. Additionally, two separate high-gain observers (HGO) are designed for the slow and fast subsystems to estimate system states. The proposed technique not only simplifies the design but also is computationally efficient. Simulations are performed in MATLAB/Simulink environment with results demonstrating that the proposed output feedback control method robustly achieves trajectory tracking.

This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds. The proposed tracker is based on a novel data association scheme guided by prediction confidence, and it consists of two key parts. First, we design a new predictor that employs a constant acceleration (CA) motion model to estimate future positions, and outputs a prediction confidence to guide data association through increased awareness of detection quality. Second, we introduce a new aggregated pairwise cost to exploit features of objects in point clouds for faster and more accurate data association. The proposed cost consists of geometry, appearance and motion components. Specifically, we formulate the geometry cost using resolutions (lengths, widths and heights), centroids, and orientations of 3D bounding boxes (BBs), the appearance cost using appearance features from the deep learning-based detector backbone network, and the motion cost by associating different motion vectors. Extensive multi-object tracking experiments on the KITTI tracking benchmark demonstrated that our method outperforms, by a large margin, the state-of-the-art methods in both tracking accuracy and speed.

Gaze tracking is widely used in fatigue driving detection, eye disease diagnosis, mental illness diagnosis, website or advertising design, virtual reality, gaze-control devices and human-computer interaction. However, the influence of light, specular reflection and occlusion, the change of head pose, especially the ever-changing human pose in natural scenes, have brought great challenges to the accurate gaze tracking. In this paper, gaze tracking in natural scenes is studied, and a method based on Convolutional Neural Network (CNN) with residual blocks is proposed, in which attention mechanism is integrated into the network to improve the accuracy of gaze tracking. Furthermore, it is tested on the GazeFollow database which contains six kinds of databases. The results show that the performance of proposed method outperforms that of other state-of-the-art methods in natural scenes. Moreover, the proposed method has better real-time performance and is more suitable for practical applications.

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.

As a typical underactuated mechanical system, offshore ship-mounted cranes are widely used to carry out the tasks of transferring cargos from one ship to another in the marine environment. Different from land-fixed cranes as well as traditional harbor cranes, offshore ship-to-ship crane systems work in two noninertial (ship) frames, while the target locations of the cargos are also inevitably influenced by the movements of the target ship. Besides, various external disturbances, which are caused by persistent sea waves, sea winds, or currents, etc., bring much more challenges to the control task of offshore ship-to-ship crane systems. To properly address these practical problems, in this article, we propose an increased adaptive neural network (NN)-based anti-swing tracking control strategy for such coordinated offshore crane systems, with a ship-motion prediction algorithm suggested to generate the target trajectories for cargos, and an adaptive NN proposed to deal with complicated unknown wave-induced disturbances. Bounded tracking performance is also guaranteed through a complete Lyapunov-based stability analysis. To the best of our knowledge, without any simplification of the original nonlinear dynamics, this article provides a high-performance adaptive control approach to deal with the anti-interference tracking control problem for offshore ship-to-ship crane systems, which are subjected to trajectories uncertainties as well as unknown wave motions disturbances. Furthermore, comparative hardware experimental results are presented to demonstrate the efficiency of the proposed control method.

This paper considers the distributed average tracking (DAT) problem that a group of agents aim to track the average of local reference signals. The communication network is strongly connected but not required to be weight-balanced. A framework of distributed estimator-based algorithm design without any initialization requirement is presented. For agents with nominal first-order (FO) dynamics, a class of distributed estimators is proposed to estimate the average of local reference signals within fixed time, with which each agent finally achieves the DAT within fixed time. For agents with second-order (SO) dynamics subject to modeling uncertainty, unmodeled dynamics, as well as external disturbances, a class of distributed estimator is proposed to estimate the average of local reference signals within fixed time, and then a local disturbance-observer-based control protocol is proposed for each agent to achieve DAT asymptotically. Different from most of the existing DAT results under general directed networks that DAT error can only be made globally ultimately bounded, the exact DAT can be achieved within fixed time or asymptotically here. Further, the idea of distri

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction., 18 pages, 13 figures, and 3 tables

The emergence of wearable devices has opened up new potentials for egocentric activity recognition. Although some methods integrate attention mechanisms into deep neural networks to capture fine-grained human-object interactions in a weak-supervision manner, they either ignore exploiting the temporal consistency or generate attention based on considering appearance cues only. To address these limitations, in this paper, we propose an enhanced attention-tracking method, combined with multi-branch network (EAT-MBNet), for egocentric activity recognition. Specifically, we propose class-aware attention maps (CAAMs) by employing a self-attention-based module to refine the class activation maps (CAMs). Our proposed method can enhance the semantic dependency between the activity categories and the feature maps. To highlight the discriminative features from the regions of interest across frames, we propose a flow-guided attention-tracking (F-AT) module, by simultaneously leveraging historical attention and motion patterns. Furthermore, we propose a cross-modality modeling branch based on an inter-active GRU module, which captures the time-synchronized long-term relationships between the appearance and motion branches. Experimental results on four egocentric activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance.