This paper investigates the leader-following consensus problem for second-order nonlinear multi-UAV systems under both fixed and Markovian switching topologies. A distributed event-triggered control protocol is proposed, which incorporates a unified triggering function that simultaneously handles intra-cluster and inter-cluster state errors. For fixed topology, sufficient conditions are derived to ensure practical fixed-time consensus. For Markovian switching topologies, a rigorous stability-guaranteeing framework is established using Multiple Lyapunov Functions and Average Dwell Time theory, which formally ensures asymptotic consensus under admissible switching signals. The absence of Zeno behavior is rigorously proved by deriving a positive lower bound on inter-event times. Comparative numerical simulations demonstrate that the proposed strategy achieves faster convergence and significantly reduces communication burden compared to conventional methods, validating its effectiveness and superiority in dynamic multi-cluster environments.

The application scenarios of automated robots are undergoing a paradigm shift from structured environments to unstructured, complex settings. In highly constrained settings like factory inspections or disaster rescue, conventional steering systems show clear drawbacks. While the four-wheel independent drive and independent steering (4WS) robot provides a variety of steering modes, which can effectively meet the needs of complex environments. However, how a 4WS robot autonomously selects different steering modes based on trajectory point information during trajectory tracking remains a challenging problem. This paper proposes a multi-modal trajectory tracking method considering the switch of steering modes, which decomposes the trajectory tracking task into two parts: mode decision-making and tracking control. The corresponding method is designed based on deep reinforcement learning. Additionally, a target trajectory random generator and corresponding training interaction environment are designed to train the model in a data-driven manner. In the designed scenario, our tracker achieve more than a 30% improvement in average tracking error across all motion modes compared with model predictive control, and the decider’s average decision position error is less than 2 cm. Extensive experiments demonstrate that our method achieves superior tracking performance and real-time capabilities compared to current methods.

With the rapid advancement of intelligent manufacturing and the rise of emerging markets, global auto-mobile exports have surged, placing unprecedented demands on logistics infrastructure. Efficient coordination of multiple robots for vehicle autonomous transfer is essential in high-density storage environments. However, conventional navigation mode, where autonomous robots navigate the entire space, often leads to inefficiencies, congestion, and increased safety risks. To address these challenges, this paper proposes a dynamic network topology framework to optimize large-scale vehicle transfers in high-density environments. The approach models free space as a network graph with directional, weighted movement costs. Leveraging yard operational characteristics, real-time transfer conditions, and robot specific capabilities, we introduce an event-triggered mechanism to update the network topology dynamically. This method continuously refines drivable space, effectively integrating yard areas with roadways to enhance routing flexibility in robot scheduling. Scenario-Based evaluations demonstrate that the proposed approach reduces traveled distance by up to 12.3% and task completion time by 19.3% compared to traditional operational networks, leading to lower operational costs and improved task efficiency. Notably, these benefits become more pronounced as the number of robots increases and the operational environment grows more complex.

In dynamic and unstructured environments, mobile robots need to generate safe and efficient trajectories in real time, which poses significant challenges due to the uncertainty of surrounding obstacles. To address this, this article presents a real-time obstacle avoidance trajectory planning method, built upon a spatio-temporal graph network that integrates temporal modeling with graph attention mechanisms. The proposed network captures both temporal dynamics and spatial structural dependencies in dynamic environments by integrating a temporal information module based on long short-term memory (LSTM) and a spatial module based on relational graph attention networks (RGAT). On the whole, the approach follows a two-phase pipeline. In the offline phase, a high-quality trajectory dataset is constructed to represent the heterogeneous state graph of the robot and surrounding obstacles. Then the dataset is used to train the spatio-temporal network, which learns to map environment-state graphs to optimal control commands. In the online phase, the trained network is deployed on the robot to perform real-time perception, decision-making, and control, forming a closed-loop trajectory optimization process. Extensive experiments in both simulated and real-world scenarios demonstrate that the proposed method achieves high-quality trajectory planning, robust obstacle avoidance, and fast generalization under multi-obstacle and sudden disturbance conditions, while maintaining low computational overhead.

To improve the stability of vehicle body dynamics on rugged roads, the majority of researchers have studied vertical force control at the terminal of the vehicle utilizing an active suspension system. However, terminal disturbance roll torque can also have a negative impact on the stability of the vehicle’s state. In this paper, a terminal roll torque control strategy (TRTCS) is proposed to enhance the stability of body attitude by controlling terminal roll torque in wheeled motion. The strategy includes impedance-based model predictive control (IMPC) and terminal posture return controller (TPRC). Firstly, IMPC analyzes the single-leg dynamic model embedded with torque impedance at the terminal. Then, the controller iterates through quadratic optimization to obtain an optimal impedance target torque that can efficiently mitigate terminal disturbance roll torque while stabilizing the terminal roll velocity, acceleration, and force on each electric cylinder. Then, TPRC generates additional smooth return speed for the terminal roll angle to the initial value through the Bezier curve and event-trigger mechanisms to estimate the terminal contact state to the ground. TPRC aims to compensate for low angular return speed due to insufficient measured torque on the suspending terminal during the downhill process. Finally, through simulation and experiment, TRTCS significantly diminishes the deviation of terminal roll torque and body attitude from target value to 25.2% and 35.8% respectively when a vehicle crosses multi-slopes compared with traditional torque impedance.Note to Practitioners—The motivation for this work is that the vibration isolation control on vehicles or wheeled robots often neglects the disturbance of the roll torque on their feet on rough roads. Unfortunately, this can damage the leg actuators of vehicles or robots, and even have a negative impact on the steadiness of the body posture. Therefore, this paper proposes a TRTCS, which controls the terminal roll torque by rotating its roll angle with high dynamic stability. The control framework has the following functionalities: 1). The terminal roll angular velocity is obtained based on an optimization function that can make the terminal torque converge smoothly to the target value and the leg actuator subjected to more distributed force. 2). A smooth roll angle correction additional speed is generated based on the Bezier curve to compensate for the slow roll angle correction speed caused by insufficient correction roll torque during the downhill phase. A series of simulations and experiments have verified that the control method has a small steady-state error in controlling the roll torque at the terminal. At the same time, under TRTCS, the peak force on the leg actuator is significantly reduced, which has a significant inhibitory effect on the actuator loss and unsteadiness of the body posture. In future research, we will further investigate the control of terminal pitch and yaw torque.

Dynamic functional connectivity (DFC) extracted from resting-state functional magnetic resonance imaging (fMRI) has been widely used for neuropsychiatric disorder classifications. However, serious information redundancy within DFC matrices can significantly undermine the performance of classification models based on them. Moreover, traditional deep models cannot adapt well to connectivity-like data, and insufficient training samples further hinder their effective training. In this study, we proposed a novel region-state masked autoencoder (RS-MAE) for proficient representation learning based on DFC matrices and ultimately neuropsychiatric disorder classifications based on fMRI. Three strategies were taken to address the aforementioned limitations. First, masked autoencoder (MAE) was introduced to reduce redundancy within DFC matrices and learn effective representations of human brain function simultaneously. Second, region-state (RS) patch embedding was proposed to replace space-time patch embedding in video MAE to adapt to DFC matrices, in which only topological locality, rather than spatial locality, exists. Third, random state concatenation (RSC) was introduced as a DFC matrix augmentation approach, to alleviate the problem of training sample insufficiency. Neuropsychiatric disorder classifications were attained by fine-tuning the pretrained encoder included in RS-MAE. The performance of the proposed RS-MAE was evaluated on four publicly available datasets, achieving accuracies of 76.32%, 77.25%, 88.87%, and 76.53% for the attention deficit and hyperactivity disorder (ADHD), autism spectrum disorder (ASD), Alzheimer's disease (AD), and schizophrenia (SCZ) classification tasks, respectively. These results demonstrate the efficacy of the RS-MAE as a proficient deep learning model for neuropsychiatric disorder classifications.

Spintronic devices, such as magnetic tunnel junctions (MTJs), are promising for space applications due to their radiation hardness and nonvolatility. However, as semiconductor technology advances, CMOS peripheral circuits are becoming vulnerable to double node upset (DNU) as well as triple node upset (TNU). This brief proposes two nonvolatile and robust latch designs primarily composed of MTJs and C-elements (CEs). Both designs offer nonvolatility and self-recovery from multiple-node upsets. Simulation results demonstrate that the proposed latches provide nonvolatility and complete protection against multiple-node upsets with balanced overhead.

Tracking dynamic targets poses a significant challenge for Unmanned Ground Vehicles (UGVs). Existing methods often lack research on multi-disturbed conditions. To address this issue, we propose a trajectory-prediction-based dynamic tracking scheme, which includes target localization, trajectory prediction, and UGV control. Firstly, an estimation algorithm based on the Extended Kalman Filter (EKF) is employed to mitigate noise and estimate the absolute states of the target accurately. To enhance robustness, we present an Adaptive Trajectory Prediction (ATP) algorithm based on prediction anchors. In this method, a quantization standard for trajectory disturbance is designed for adaptive control. Subsequently, we iteratively solve prediction anchor points based on two motion models to robustly predict the target trajectory even in the presence of unknown disturbances. Finally, the Linear Time-Varying Model Predictive Control (LTV-MPC) is utilized in the UGV controller for dynamic tracking. Experimental results demonstrate that the ATP exhibits superior prediction robustness and accuracy in perturbed environments compared to other prediction algorithms. In addition, the proposed scheme effectively achieves dynamic tracking of the Unmanned Aerial Vehicle (UAV) by the UGV under multi-disturbed conditions. Specifically, when the target moves at a speed of 1.0 m/s, the UGV can maintain a tracking error within 0.346 m.

In the labor-intensive Roll-On/Roll-Off (RO/RO) terminal environment, research on vehicle transport robots with mobility, stability, and reliability is receiving increasing attention. This paper presents a novel control framework for a Straddle-Type Dual-Body vehicle transfer robot. Initially, fine segmentation and processing of point clouds from different areas of the robot are performed, switching perception strategies for different areas based on event triggers. For target pose estimation, a traversal-based point cloud matrix fitting algorithm is designed. Additionally, for loading and unloading operations, a docking controller based on real-time target detection is developed to ensure minimal lateral and angular errors during target docking. Finally, the proposed control framework is validated through operations of the vehicle transfer robot in outdoor RO/RO terminal yards. Experimental results indicate that the average docking error remains within 3cm, with a 6.5% reduction in docking time under the same conditions. The docking precision and stability performance of the vehicle transfer robot surpass traditional methods, demonstrating satisfactory performance.