Range-Only Simultaneous Localization and Mapping (RO-SLAM) is crucial for mobile robot navigation. However, the lack of directional information in distance measurements causes significant nonlinearity and convergence issues. These challenges are particularly prominent during beacon initialization. During this phase, joint estimation of the robot pose and beacon positions becomes difficult. To tackle this problem, we propose a novel initialization algorithm using particle filtering. This algorithm models beacon position uncertainty using von Mises–Fisher (vMF) distributions. It also introduces an innovative resampling strategy with dynamically adjusted particle numbers. Building on this initialization, we construct a comprehensive 3D RO-SLAM framework. Both simulation and real-world experiments show that the proposed algorithm provides accurate and efficient estimation of robot pose and beacon positions. These results demonstrate improvements in both initialization accuracy and speed.

The inverse dynamic games problem is to model expert demonstrations by identifying the underlying cost functions of multiple agents from observed trajectories of their dynamic game interactions. This article investigates discrete-time, finite-horizon linear-quadratic (LQ) problems where both the state weight matrix and input weight matrix are unknown, with the presence of both process noise and observation noise. In addition, each player's cost function incorporates a player-specific, unknown linear term with respect to the state. Under this framework, first, sufficient conditions are established for the solvability of the weight matrices. Subsequently, it is proved that the inverse dynamic games problem involving heterogeneous unknown target states is structurally identifiable, unaffected by process noise. Building on the necessary conditions for Nash equilibrium solutions in forward problems, the estimation of the cost function parameters is formulated as a nontrivial solution to a homogeneous linear estimation problem, which can be implemented in a distributed manner. Furthermore, the proposed estimator achieves statistical consistency under the influence of observation noise. The effectiveness is illustrated through a multivehicle spring-coupled dynamic game and an interactive steering control scenario.

This article develops a scheme to tackle the safe optimal formation tracking issue for multiple fixed-wing uncrewed aerial vehicles (UAVs) with external disturbances and asymmetric control constraints. To ensure safety constraints in collision avoidance, a safe set is first constructed by a super level set of a continuously differential function, following a novel control barrier function (CBF) to characterize the safety. Subsequently, we transform the safe optimal formation tracking control into a constrained zero-sum (ZS) differential game to mitigate the destabilizing effects of the disturbances, where the cost function is constructed in a nonquadratic form to cope with asymmetric input constraints. Particularly, the designed CBF is integrated into the cost function to penalize the unsafe behavior, and a damping coefficient is included to balance the optimality and safety. Afterwords, a critic-only reinforcement learning (RL) strategy is developed to learn the robust safe Nash policy, where the critic weights are updated by applying experience replay technology, thus avoiding the requirement for persistence of excitation condition. Moreover, the stability and forward invariance of the safe set of the presented scheme are also verified. Finally, simulation examples are provided to substantiate the validity of the control scheme.

This paper proposes an approach for fixed-time (FxT) adaptive optimized formation control of nonlinear multi-agent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To address system uncertainty and state constraints while achieving optimality in FxT settings, the paper presents a novel adaptive estimation and analysis. The proposed approach first introduces a tan-type nonlinear mapping to handle state constraints, eliminating the feasibility condition of the conventional barrier Lyapunov function method. Next, the actual optimal controller is iteratively designed using the identifier-actor-critic structure and optimized backstepping method, with neural approximators used to learn system uncertainty. Finally, a monotonically decreasing function is constructed to prove that the designed actor-critic update laws have an upper bound, which is essential for stability analysis. The proposed scheme can ensure that the formation is realized at a fixed time while optimizing a given performance index and meeting the constraint requirements. The simulation results verify the effectiveness of the proposed approachNote to Practitioners—There are inevitable system constraints and model uncertainties in actual physical systems, which may degrade the operational performance of the system. In addition, for practical requirements, designers are eager for multiple agents to achieve the required formation performance at a fast convergence speed. While meeting the requirements of system constraints, it is of practical and theoretical significance to improve the convergence speed and robustness of MASs formation and ensure optimal performance. For this reason, this paper focuses on proposing an effective adaptive FxT-optimized formation control scheme to enhance system performance and convergence speed, which combines nonlinear mapping to address full-state constraints. To achieve optimality under FxT settings, an optimal controller with learning laws is first designed using an identifier-actor-critic structure, in which the identifier is used to learn uncertainty. Then, by constructing a quadratic function, it is proved that the estimation error of the learning law is bounded, thereby forming a new adaptive estimation and analysis scheme.

Virtual coupling technology has been widely concerned due to its potential in improving the operational efficiency of rail transit. To mitigate the effects of measurement error and actuation fault on coordinated train operation, this paper proposes a distributed fault-tolerant control strategy for the virtual coupling train system by the integration of moving horizon estimation (MHE) and model predictive control (MPC). Considering potential faults for virtually coupled trains during operation, a faulty model is developed to capture measurement errors and loss of actuator effectiveness. A fault-tolerant control strategy is designed that consists of two modules, i.e., fault estimation and fault-tolerant control. The MHE method is employed in the fault estimation module to estimate faults by solving a local quadratic optimization problem based on historical data. In the fault-tolerant control module, a reconfigured train tracking control problem is constructed under the MPC framework on the premise of real-time estimation. An accelerated alternating direction method of multipliers algorithm is further designed to embed with the fault-tolerant model predictive controller to calculate the control commands in a distributed manner. Numerical experiments are conducted and demonstrate the effectiveness of the proposed approach.

This article studies the distributed adaptive event-triggered consensus control problem of linear multiagent systems. A strong non-Zeno mixed adaptive dynamic event-triggering scheme is proposed, which guarantees a strictly positive minimum interevent time (MIET) between any two consecutive events. A model-based event-triggered fully distributed adaptive control law is presented without using prior global information about the communication topology. Moreover, a hybrid system model is constructed to facilitate the stability analysis of the closed-loop system. It is shown that the proposed control strategy can achieve asymptotic consensus of all agents via intermittent communication in a fully distributed way, while guaranteeing the strictly positive MIET property. Finally, the effectiveness of the designed control method is illustrated by a simulation example.

Cooperative Adaptive Cruise Control (CACC) based vehicle platooning can increase the safety and efficiency of traffic. This work looks into the communication and control problems of vehicle platooning, and proposes a control and communication co-design for CACC. First, an integrated radar system is presented. This system integrates sensing of relative position, speed, and communication between a predecessor and its follower. Second, a working scheme for the integrated radar system is presented. This scheme allows the radar systems to switch periodically between different working modes without interferences from other modes. Therefore, the relative position, speed, and communication can be asynchronously periodically updated to the controller. Third, a periodic event-triggered control approach is presented. This approach allows asynchronous periodic sampling of the output, and is deeply co-designed with the radar system and its working scheme. Delays are also considered in the control approach. The co-design CACC approach can guarantee the vehicle platoons to be string stable. A numerical example has shown the feasibility of the approach.

Most anchor-free methods perform object detection using dense recommendation, which assumes that one point can simultaneously conduct accurate category prediction and regression estimation. However, due to different task drivers, valid features for classification and regression may locate at distinct areas in the training phase. This problem is called feature misalignment. To solve it, we propose a new feature alignment method based on anchor-free object detector. Firstly, a global receptive field adaptor (G-RFA) is designed by incorporating the feature pyramid networks (FPN) with the global attention mechanism, and forward features are further fine-tuned with a deformable-subnet (De-Subnet) to remove the influence of redundant contextual information. Then, a new feature filter strategy with a misalignment score is proposed to guide the network to focus on sampling points with aligned features. In addition, we establish mutually independent multi-layer quality distributions to model the priori information of an object on different FPN levels. Equipped with our method, the classification and regression features are aligned, and the generated foreground weight map converges to the centers of classification and regression heatmaps. Experimental results show that without bells and whistles, our method achieves 49.3% AP on MS COCO test-dev under the default 2x training schedule, outperforming related methods. Besides, experiments on PASCAL VOC demonstrate the generalization ability of our method. Code is available at https://github.com/GFENGG/featurealign.

In this article, a consistent subspace identification method (SIM) is proposed for block-oriented errors-in-variables Hammerstein systems. Due to that the existing SIMs using parity subspace based on noisy measurements may result in biased parameter estimates, we propose a scheme for the consistent system parameter estimation, which estimates the noise-free Hankel matrix using available noisy measurements and noise variances. A 2-D search method is proposed to estimate the unknown noise variances from available noisy measurements. After that consistent estimations of the Hammerstein system parameters can be then retrieved from the estimated noise-free Hankel matrix following the same algorithm framework of the existing SIMs using parity subspace. Two simulation examples are included to support the effectiveness and merits of the proposed method.

Traditional monocular vision localization methods are usually suitable for short-range area and indoor relative positioning tasks. This paper presents MGG, a novel monocular global geolocation method for outdoor long-range targets. This method takes a single RGB image combined with necessary navigation parameters as input and outputs targets' GPS information under the Global Navigation Satellite System (GNSS). In MGG, we first design a camera pose correction method via pixel mapping to correct the pose of the camera. Then, we use anchor-based methods to improve the detection ability for long-range targets with small image regions. Next, the local monocular vision model (LMVM) with a local structure coefficient is proposed to establish an accurate 2D-to-3D mapping relationship. Subsequently, a soft correspondence constraint (SCC) is presented to solve the local structure coefficient, which can weaken the coupling degree between detection and localization. Finally, targets can be geolocated through optimization theory-based methods and a series of coordinate transformations. Furthermore, we demonstrate the importance of focal length on solving the error explosion problem in locating long-range targets with monocular vision. Extensive experiments on the challenging KITTI dataset as well as applications in outdoor environments with targets located at a long range of up to 150 meters show the superiority of our method.

This paper proposes a multi-hop distributed sensor network localization method based on UWB ranging measurements. Different from the positioning algorithm suitable for known indoor environments, this algorithm is designed for the exploration needs of unknown areas. Based on the barycentric coordinates of the tetrahedron, the expansion of the sensor network is realized through the multi-hop of the UWB sensor. The proposed algorithm can be applied for three-dimensional space. By designing time-varying weight coefficients and combining Taylor expansion, the localization performance is enhanced. Finally, a numerical simulation is given to verify the effectiveness of the proposed algorithm.

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years. Some existing IPSs have already adopted ELM, but it suffers from signal variation and environmental dynamics in indoor settings. In this paper, extreme learning machine with dead zone (DZ-ELM) is proposed to address this problem. The consistency of this approach should be applied is studied. Simulations are also conducted to compare the performance of DZ-ELM and ELM. Lastly, real-world experimental results show that the proposed algorithm can not only provide higher accuracy but also improve the repeatability of IPSs.

This paper studies the blind identification of multi-channel FIR systems in the context of sensor networks. Distributed identification algorithms are developed for both noise-free and noise-contaminated networked systems. The proposed algorithms distribute the data storage and computational load among multiple agents connected by a specified topology, and are fulfilled via information exchanges among neighboring agents without the need of fusion centers. In the presence of measurement noises, a stabilized distributed algorithm is provided which can avoid trivial estimations of the multiple channels. In addition, convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.