The Manned Submersible Vehicle (MSV) is a critical tool for combating submarine oil pipeline leaks. This paper adopts the core idea of a goal-oriented control system (GOCS). We propose a dual control for exploration and exploitation (DCEE) framework. The DCEE guides the submersible to autonomously collect rich measurement data, enabling accurate and efficient source-term estimation. For the first time, we developed a plume model adapted to submarine pollutant dispersion to guide the MSV equipped with the DCEE framework for source pinpointing. Under uncertainty of leakage parameters, a search strategy is designed to maximize the information obtained to detect leak sources. Furthermore, we employ a disturbance observer-based control (DOBC) algorithm in the low-level control to compensate for external disturbances. It ensures stable path tracking and yields a closed-loop autonomous search system for underwater operations. The simulation results show that our method can guide MSV to the leak source in complex and unknown submarine environments, which realizes rapid and accurate localization.

This study proposes a new adaptive Kalman filter for high-precision cooperative target tracking based on the Singer acceleration model. In cooperative scenarios, the maneuver time constant$\alpha$is known in advance through mission planning or communication between targets, allowing a physically consistent and structure-preserving noise modeling. Traditional approaches often necessitate the estimation of the full noise covariance matrix, which can be computationally intensive and prone to inaccuracies and spurious state correlations. To overcome this limitation, this work elaborates a novel online power spectral density (PSD) estimation scheme. Using known$\alpha$to reduce the number of unknown variables, the new approach can improve the accuracy and reliability of the estimation. Numerical experiments in cooperative target tracking demonstrate that the refined algorithm achieves robust adaptability to dynamic noise that varies over time, provides high-precision state estimates, and maintains low computational complexity.

Some real-world transportation systems require moving robots from their start points to goal points without collision, which can be formalized as the multi-agent pathfinding (MAPF) problem. The Priority Inheritance with Backtracking (PIBT) algorithm is an efficient approach for MAPF with low-degree polynomial time complexity and proven completeness. PIBT employs a dynamic prioritization scheme to determine the order of sequential agent planning. However, this scheme may lead to low solution quality, which is measured by the sum of time steps each agent takes to reach its goal for the first time. In this work, we propose two greedy variants of the PIBT algorithm that improve solution quality while preserving completeness: Backflow-based Greedy PIBT (GPIBT-B) and Reduction-based Greedy PIBT (GPIBT-R). GPIBT-B introduces a backflow mechanism that allows a determined agent to adjust its plan during the planning of another agent, achieving higher solution quality while maintaining the same low time complexity as PIBT. GPIBT-R formulates the planning problem as a Mixed-Integer Linear Programming (MILP), enabling the use of efficient MILP solvers to find high-quality solutions. Experimental results show that GPIBT-B substantially improves solution quality with minimal additional computation time, while GPIBT-R achieves even better solution quality at the cost of increased computational time.

In practical applications, the degradation behavior of lithium-ion batteries exhibits significant differences due to variations in operating conditions. Meanwhile, the scarcity of labeled data poses considerable challenges for capacity prediction in terms of both accuracy and generalization. To address these issues, this article proposes a cross-domain semisupervised capacity prediction framework that integrates multigranularity feature modeling with a confidence controlled pseudolabel selection mechanism. Specifically, the proposed method enhances the model’s ability to capture the granularity of nonlinear degradation trends in battery capacity, thereby improving prediction accuracy and stability. In addition, a pseudolabel learning strategy based on confidence filtering and stagewise regulation is designed to dynamically guide high-quality pseudolabels in the target domain into training, effectively reducing the risk of noisy label propagation. Experiments conducted on eight tasks across two heterogeneous battery datasets demonstrate R$^{2}$improvements of 1.3%–8.7% and Mean Absolute Error (MAE) reductions of 38%–80%, validating the practical potential of the proposed method under complex degradation scenarios.

In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Autonomous aerial vehicles (AAVs) can improve inspection efficiency. However, the data transmission delay in the existing cloud-only (CO) artificial intelligence (AI) mode fails to meet the low latency requirements for real-time performance. Therefore, we propose a computer vision-based AI of Things (AIoT) system to detect antenna interference sources for AAVs. The system adopts an optimized edge-cloud collaboration (ECC+) mode, combining a keyframe selection algorithm (KSA), focusing on reducing end-to-end latency (E2EL) and ensuring reliable data transmission, which aligns with the core principles of ultrareliable low-latency communication (URLLC). At the core of our approach is an end-to-end antenna localization scheme based on the tracking-by-detection (TBD) paradigm, including a detector (EdgeAnt) and a tracker (AntSort). EdgeAnt achieves state-of-the-art (SOTA) performance with a mean average precision (mAP) of 42.1% on our custom antenna interference source dataset, requiring only three million parameters and 14.7 GFLOPs. On the COCO dataset, EdgeAnt achieves 38.9% mAP with 5.4 GFLOPs. We deployed EdgeAnt on Jetson Xavier NX (TRT) and Raspberry Pi 4B (NCNN), achieving real-time inference speeds of 21.1 (1088) and 4.8 (640) frames/s (FPS), respectively. Compared with CO mode, the ECC+ mode reduces E2EL by 88.9%, increases accuracy by 28.2%. Additionally, the system offers excellent scalability for coordinated multiple AAVs inspections. The detector code is publicly available athttps://github.com/SCNU-RISLAB/EdgeAnt.

The propulsion system of the submersible is critical for deep-sea operations, yet traditional supervised fault diagnosis methods falter due to the scarcity and incompleteness of abnormal sensor data. To address this, we propose an unsupervised fault diagnosis method based on the GANomaly model, trained exclusively on normal operational data. Specifically, we introduce a temporal–spatial data transformation to preprocess multisensor time-series signals, converting them into 2-D representations that capture intersensor correlations and temporal dynamics. By learning healthy patterns from normal data only, the GANomaly approach enables anomaly detection through its reconstruction limitations on faulty data. Building on this capability, a dual-threshold technique using anomaly and component scores accurately identifies and localizes faults in both time and space. Tested on synthetic and real fault data from a five-year period of dives, this method achieves robust fault detection and localization with 93.6% accuracy, with results validated against on-site maintenance records.

The Internet of Things (IoT) application has significantly enhanced the operational efficiency and communication capabilities of large-scale networked uncrewed surface vehicles (USVs). Nevertheless, resource-limited communication relay stations and potential Denial of Service (DoS) attacks pose severe threats to the reliability and security of IoT systems. Given these challenges, this article proposes a dynamic time assignment communication mechanism (DTACM) for USVs to manage communication resources and maintain formation security. The DTACM categorizes USVs into distinct groups and ensures interleaved intergroup communication. This prevents excessive individuals from occupying communication stations simultaneously, thereby guarding against potential communication delays and packet loss. Furthermore, to assess the damage caused by DoS attacks, this study introduces the average dwell-time automaton and time-ratio monitor into a hybrid system. These tools facilitate the analysis of system stability and resilience under malicious attack scenarios. Finally, the theoretical analysis and simulation results show the effectiveness of the proposed control scheme.

This paper addresses the attitude consistency problem in multi-hydrofoil crafts, considering actuator faults, false data injection, stochastic ocean wave disturbances, and topological propagation effects. We propose a game-theoretic fault-tolerant control (FTC) framework integrating a composite controller with a specific performance index. In this framework, both local and neighboring node information is considered. The desired controller, along with non-ideal anomalies and disturbances, is treated as participants, transforming the FTC problem into a multi-player non-cooperative game. Moreover, a single critic neural network (NN) is utilized to alleviate the challenges associated with solving a partial differential equation, thereby facilitating the derivation of the ideal control law. Compared to traditional local information-based control methods, the proposed approach incorporates neighborhood information. It integrates faults, disturbances, and propagation effects within a unified framework for optimal control, enhancing FTC effectiveness. We conduct comparative experiments using a four-craft scenario on the dSPACE platform. The results demonstrate that the proposed method reduces the mean absolute deviation (MAD) of local tracking error by at least 28.00% and the standard deviation (STD) by at least 7.76% compared to the other two methods.

This paper investigates the attitude tracking control problem for spacecraft. A tanh-type self-learning control (TSLC) approach with variable learning intensity (VLI) is proposed, which avoids saturation while overcoming previous algorithms’ long response time disadvantage. Unlike the previously introduced VLI method, the enhanced TSLC does not tweak the learning intensity based on the previous controller output. Instead, it relates learning intensity to an intermediate variable directly related to the system state and tunes the learning intensity using a tanh-type function. Since the system state reflects the tracking error in real-time, the transformed tanh-type function has a higher decay rate than the exponential function, which not only significantly reduces the saturation response but also improves the response speed and achieves higher steady-state accuracy. Simulation proved TSLC’s superiority, considering adverse actuator factors such as dead zone, bias torque, and saturation. The proposed approach has also been validated on the Quanser helicopter platform, confirming its better performance.

This article investigates loosely coupled inertial navigation system/global positioning system (INS/GPS) integration for land vehicle navigation. To achieve navigation with higher accuracy and lower computational complexity, we present an integration solution using factor graph optimization (FGO) based on the graphical state-space model (GSSM). This solution is referred to as GSSM-FGO. Compared with traditional methods, the unique specialty of our work lies in both modeling and problem-solving aspects under the assumption of calibration parameter invariance. Specifically, we suggest that the time-series state-space model is not always suitable for widely existing constant calibration parameters. Thus, we propose GSSM as a more flexible and accurate state description by extracting the constant states as singular nodes. The FGO is adopted to manage this novel graphical model, while traditional filter-based algorithms fail when faced with the cyclic model structure. The universality of our approach is validated through a real-world land vehicle navigation dataset, featuring four distinct-grade inertial measurement units. Compared to the methods based on extended Kalman filter and FGO with the traditional state-space model, our approach demonstrates a substantial enhancement in estimation accuracy and computational speed.

Communication connectivity is the main factor that affects networked unmanned surface vehicles’ (NUSVs) formation maneuverability. When vehicles are maneuvered for obstacle avoidance, distances between neighbors may exceed the limits of allowable communication ranges, causing communication interruption. Within this context, this paper proposes an event-driven fully distributed affine formation maneuver control (FDAFMC) scheme for NUSVs undergoing intermittent network connectivity. Firstly, a distributed observer is developed to divide the whole system into the cooperative localization subsystem and the tracking subsystem. A node-based adaptive gain is then developed in the cooperative localization layer to synthesize the intermittent event-triggered mechanism. Based on this framework, communication interruption occurring both in position and yaw channels can be considered integrally. Furthermore, by resorting to a preset${\mathcal { L}}_{2}$gain in the tracking layer, system stability, and robustness are tactfully ensured. The prominent feature of the proposed method lies in ensuring maneuver flexibility without assuming a constantly maintained network connectivity. Simulation results show the efficacy of the FDAFMC strategy.

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://github.com/SCNU-RISLAB/MF-MOS.

Rapidly recognizing water meter readings is crucial for intelligent water management systems. Despite the widespread availability of smart water meters, the lower cost of word-wheel water meters means they continue to be used in most cases. As a result, manual reading and data review processes persist, hindering efficient management of water resources. Traditional recognition methods have been hampered by complex algorithms and insufficient robustness. This paper proposes a deep learning-based detection and recognition method for word wheel water meters, which involves dividing the reading process into three stages: detection, correction, and recognition. We have targeted algorithm design to suit the unique environment where the water meter is located. We then made specific refinements and improvements to the recognition method to improve the performance. The method achieved an impressive segmentation accuracy of 98.2% and a recognition accuracy of 98.7% on a self-built dataset collected throughout Hangzhou, China. Additionally, it boasts a small model size and a short inference time, showcasing excellent efficiency. By streamlining manual meter reading and data review processes, our approach holds great potential for facilitating effective water resource management.

The generalized rigid registration problem in high-dimensional Euclidean spaces is studied. The loss function is minimized with an equivalent error formulation by the Cayley formula. The closed-form linear least-square solution to such a problem is derived which generates the registration covariances, i.e., uncertainty information of rotation and translation, providing quite accurate probabilistic descriptions. Simulation results indicate the correctness of the proposed method and also present its efficiency on computation-time consumption, compared with previous algorithms using singular value decomposition (SVD) and linear matrix inequality (LMI). The proposed scheme is then applied to an interpolation problem on the special Euclidean group SE(n) with covariance-preserving functionality. Finally, experiments on covariance-aided Lidar mapping show practical superiority in robotic navigation.

This work studies the${H}_{\infty }$-based minimal energy control with a preset convergence rate (PCR) problem for a class of disturbed linear time-invariant continuous-time systems with matched external disturbance. This problem aims to design an optimal controller so that the energy of the control input satisfies a predetermined requirement. Moreover, the closed-loop system asymptotic stability with PCR is ensured simultaneously. To deal with this problem, a modified game algebraic Riccati equation (MGARE) is proposed, which is different from the game algebraic Riccati equation in the traditional${H}_{\infty } $control problem due to the state cost being lost. Therefore, a unique positive-definite solution of the MGARE is theoretically analyzed with its existing conditions. In addition, based on this formulation, a novel approach is proposed to solve the actuator magnitude saturation problem with the system dynamics being exactly known. To relax the requirement of the knowledge of system dynamics, a model-free policy iteration approach is proposed to compute the solution of this problem. Finally, the effectiveness of the proposed approaches is verified through two simulation examples.

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

In leader-following formation, the leader plays a central role and is more vulnerable to malicious attacks. To effectively protect the leader, this article investigates an event-based formation control problem of autonomous surface vehicles (ASVs) with multiple attackers and actuator failure. A novel adaptive formation keeping and interception scheme is developed for ASVs. In this scheme, a formation keeping controller and an interception controller are designed under a unified framework by sharing same event-triggered mechanism and fault-tolerant structure. To bridge these two controllers, a degree and distance driven formation interception ($\text{D}^3$FI) strategy is designed by generating defenders. It is shown that with the proposed scheme, all attackers are intercepted without affecting formation keeping of remaining ASVs if the original topology is connected. Advantages of our scheme are: 1) stable controller switching is ensured during the formation cooperation and 2) the fault-tolerance is obtained with lower actuator updating frequencies and least number of adaptive parameters for each ASV.

This article presents integral-type edge event- and self-triggered policies for Lipschitz nonlinear multiagent systems, in which only edge states are employed by all controllers. An integral-type triggering function is designed to determine event instants, and the considered system can achieve Zeno-free triggering. An integral-type edge self-triggered policy is then designed to avoid sensors’ continuous measurements. Compared to traditional event-triggered schemes, the proposed strategies have relaxed triggering conditions and lowered the event frequencies. Also, the proposed edge self-triggered algorithm can avoid the requirement for continuous measurement error monitoring. Numerical simulations are given to demonstrate the effectiveness of the theoretical conclusions.