Search Results (626)

Maritime communication systems are among the most important infrastructure of global maritime safety and security. They consist of very high frequency (VHF) radio, the Global Maritime Distress and Safety System (GMDSS), contemporary satellite nets, Automatic Identification System (AIS) networks, and the emerging VHF Data Exchange System (VDES). These systems are essential for distress signaling, navigational coordination, and vessel traffic management. As maritime operations are experiencing accelerated digitalisation, the safety and security dimensions of maritime communication systems have attracted substantial and growing scientific attention. This study presents a comprehensive literature review and bibliometric analysis of the safety and security of maritime communication systems. Guided by the PRISMA 2020 guidelines and Systematic Literature Review (SLR) methodology, a structured search was conducted across three major scientific databases: Scopus, Web of Science (WoS), and IEEE Xplore. Starting from a raw pool of 6648 records retrieved between 2000 and 2026, the dataset was reduced through successive filtering to a final body of 68 high-relevance publications. Bibliometric analysis reveals a significant upward publication trend from 2015 onwards, with a marked acceleration after 2019. Thematic analysis identifies seven principal research clusters: GMDSS modernisation, AIS safety and security, VDES and VHF next-generation systems, maritime cybersecurity, satellite communications, risk assessment frameworks, and emerging technologies, including artificial intelligence and autonomous vessel communications. The review identifies significant research gaps, including the absence of integrated cross-system risk frameworks, insufficient attention to human factors in cybersecurity, limited studies addressing emerging regulatory, legal governance components and a brief analysis of the maritime communications market. This study provides a structured foundation for future research and policy development in maritime communication security. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

Autonomous driving in unstructured environments faces challenges such as missing road boundaries, terrain variations, random obstacle distributions, and complex vehicle–terrain interactions, making it difficult to achieve safe navigation by relying on lane-level priors from structured roads. To address the problems of the relative separation between traversability analysis and trajectory planning, the ineffective propagation of perception uncertainty, and the insufficient scene adaptability of coupling mechanisms, this paper takes traversability as the main thread and systematically reviews the research progress of perception–planning coupling mechanisms in unstructured environments. First, traversability analysis methods based on geometric terrain, semantic understanding, and physical dynamics are reviewed, and the representation and propagation mechanisms of uncertainty in the perception–planning chain are analyzed. Second, the role of traversability information in global path search, local trajectory optimization, and data-driven planning is discussed, and the applicable boundaries of different coupling architectures are summarized from the perspectives of representation level and system organization form. Finally, datasets, simulation platforms, and evaluation metric systems are summarized, and a risk-state-oriented adaptive perception–planning coupling framework is proposed to dynamically adjust coupling strength based on risk-state information, thereby improving the safety, interpretability, and environmental adaptability of autonomous driving in unstructured environments. Full article

LiDAR-based place recognition is critical for long-term autonomous navigation in Global Navigation Satellite System (GNSS)-denied environments, yet existing methods struggle to balance accuracy and efficiency under substantial yaw rotations. This paper proposes a robust framework based on a multi-channel polar bird’s-eye-view (BEV) representation. Under yaw-dominated revisits, the polar BEV image transforms yaw rotation into cyclic column shifts, providing a useful structural prior for rotation-equivariant feature extraction. Raw point clouds are projected onto polar BEV grids encoding density, height, and intensity. A rotation-equivariant feature extractor comprising a Radial Compression Module and a rotation-equivariant Transformer module captures long-range azimuthal dependencies via Conditional Positional Encoding and Circular Relative-Position Bias. The equivariant features are aggregated by NetVLAD into a compact global descriptor, trained end-to-end with a hard-example mining triplet loss. Extensive experiments on the public KITTI and NCLT datasets, as well as our self-constructed LiDAR Place Recognition Revisit (LPRR) dataset, demonstrate competitive performance on KITTI and superior performance on NCLT and LPRR among the compared methods. The proposed framework achieves a favorable trade-off between performance and computational cost, and shows promising cross-dataset generalization on the evaluated NCLT and LPRR datasets without fine-tuning. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

As autonomous driving technology expands from structured urban roads to unstructured outdoor environments, precise understanding of complex terrain has become a critical requirement for ensuring safe vehicle navigation. However, outdoor environments are characterized by high dynamics, drastic illumination variations, ambiguous category boundaries, and prohibitive annotation costs, making traditional supervised learning methods that rely on large amounts of pixel-level annotations difficult to generalize. In this paper, we propose a novel dual-path bidirectional interactive encoder, termed LapDINO, that effectively combines the strong semantic generalization capability of the self-supervised foundation model DINOv3 with the multi-scale frequency analysis capacity of the Laplacian pyramid. Specifically, we leverage DINOv3 to extract global semantic features as a “semantic map”, while simultaneously obtaining multi-scale high-frequency details through Laplacian pyramid decomposition as “structural contours”. Building upon this, we design a bidirectional cross-attention fusion mechanism that enables dynamic interaction and mutual refinement between semantic information and geometric details. Furthermore, we introduce a multi-branch attention enhancement module that extracts pyramid features from three complementary perspectives. To address domain shift, we design lightweight visual adapters that enable efficient fine-tuning of the frozen DINOv3 backbone. Finally, we construct two off-road terrain segmentation datasets, VOTD and VOCD, to facilitate research in this domain. Experimental results demonstrate that the proposed method achieves state-of-the-art performance, striking an optimal balance between accuracy and computational efficiency, thereby providing a robust and efficient engineering solution for terrain perception in off-road environments. Full article

Global Navigation Satellite Systems (GNSS)-denied environments pose significant challenges for autonomous drone navigation, requiring robust visual localization systems capable of real-time performance. Existing approaches either sacrifice accuracy for speed or fail to adapt to varying flight altitudes and orientations, limiting their practical deployment. We present Real-Time Elevation and Orientation-Aware Localization Architecture (REOLA), a visual localization system that combines similarity-driven autonomous window sizing, element-wise correlation-based orientation detection, and reinforcement learning with human feedback (RLHF) enhancement for publicly available satellite imagery. On desktop hardware (i7-10700K + RTX 3070), the REOLA achieved approximately 59 FPS performance with sub-5-m accuracy across diverse flight conditions through intelligent similarity-based matching, combined with efficient MobileNet-V3 embeddings and FAISS similarity search. For embedded deployment on NVIDIA Jetson Orin Nano, the system achieved 22.5 FPS, meeting real-time requirements for autonomous drone localization. The system autonomously selects optimal window sizes corresponding to the current elevation and determines drone orientation through element-wise correlation scoring across discrete rotation angles. Enhanced through RLHF, the REOLA achieved a $97.1\%$ success rate (sub-5-m localization) while processing frames in 17 milliseconds on desktop hardware (44.4 ms on embedded hardware), providing a substantial margin over real-time requirements. The approach demonstrates particular superiority over traditional keypoint-based methods in challenging environments with repetitive patterns such as agricultural fields, rocky mountains, dense forests, and grasslands, where conventional keypoint detection struggles. We explicitly identify featureless sand dune deserts and open-sea or coastal water flights as out of scope, since the reference satellite imagery in those regimes does not contain stable landmarks. Full article

Autonomous navigation is a key requirement for underground mine automation, where the choice of a suitable global path planner plays a significant role. In this study, four representative planning approaches—Dijkstra’s algorithm, A*, Rapidly exploring Random Tree (RRT*), and Particle Swarm Optimization (PSO)—were evaluated on a differential-drive mobile robot within the ROS navigation framework. The algorithms were tested in two simulated underground environments: a room-and-pillar layout with relatively open space and multiple path alternatives and a narrow tunnel scenario designed to reflect more constrained mining conditions. The results indicate that Dijkstra’s algorithm consistently produced the shortest paths with the lowest computation times, while A* showed comparable performance with slightly higher computational effort. RRT* required modifications to operate effectively in narrow tunnels and exhibited significantly longer planning times. PSO, although capable of generating near-optimal solutions in open spaces, showed limitations in constrained environments due to collision handling and path feasibility issues. Differences in replanning behavior were observed when unknown obstacles were introduced. Overall, graph-based planners such as A* and Dijkstra’s algorithm demonstrated more stable and predictable performance. Future work will focus on validating these findings in real mining environments, particularly considering wheel slippage, sensor noise, and path generation challenges in narrow tunnel conditions. Full article

Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies to enhance initial solution quality, balance global and local search, and avoid local optima. The proposed algorithm generates shorter, smoother, and safer paths, as demonstrated through benchmark tests, multi-scale grid-map simulations, and real-world AGV experiments. In terms of path length and computational efficiency, the enhanced algorithm significantly outperforms the original HHO, reducing average path length by 10.81% and average travel time by 11.94%. These results demonstrate that the proposed method provides a practical and reliable solution for autonomous warehouse navigation and significantly improves AGV path-planning performance. Full article

Three-dimensional (3D) path planning for Unmanned Underwater Vehicles (UUVs) in typical marine operating conditions presents high-dimensional, non-convex optimization challenges due to undulating seabed topography, underwater threat sources, and coupled multi-physical constraints. Existing studies lack multi-strategy collaborative optimization mechanisms specifically designed for UUV 3D marine navigation constraints, thereby hindering the simultaneous achievement of real-time performance, safety, and energy efficiency in path planning. This paper first develops a comprehensive multi-dimensional cost function based on the dynamic characteristics of UUV underwater 3D navigation, operational rules for typical marine operating conditions, and safe navigation requirements through mathematical modeling, thereby formally transforming the UUV 3D path planning problem in typical marine operating conditions into a multi-constrained nonlinear global optimization problem. To address this challenge, an Adaptive Cooperative WMA (ACWMA) is proposed. The key improvements include: (i) an adaptive parameter switching and Lévy flight disturbance mechanism to balance exploration and exploitation capabilities; (ii) an optimal value leadership strategy to accelerate convergence; and (iii) a team collaborative learning mechanism to enhance population optimization efficiency. Algorithm benchmark performance is validated using the CEC 2017 standard test suite, while comparative and ablation experiments are conducted in multi-gradient complex marine 3D scenarios. The statistical significance of the algorithm performance improvement is verified using the Wilcoxon rank-sum test. The proposed ACWMA achieves a significant performance improvement of 8.71% over the suboptimal WMA in terms of core performance metrics and generates low-energy-consumption 3D paths that satisfy multiple constraints. These findings provide valuable engineering insights for 3D path planning in UUV autonomous operations within typical marine operating conditions. Full article

Home indoor environments are characterized by alternating open spaces and obstacle-cluttered regions, which pose critical challenges to the autonomous navigation of home service robots. Existing hybrid path planning algorithms generally suffer from three core limitations: low global search efficiency, weak global-local planning coordination, and poor dynamic scene adaptability. To tackle these issues, this paper presents a novel hierarchical path planning framework combining an enhanced Jump Point Search (JPS) and a fuzzy-optimized Dynamic Window Approach (DWA). In the global planning layer, an adaptive Octile heuristic JPS based on local obstacle density is designed to reduce redundant node expansion and accelerate global path search, with a bounded suboptimality guarantee. To bridge global and local planning, a look-ahead distance-based dynamic waypoint selection strategy is developed to match the optimal waypoint in real time according to the robot’s motion state and environmental complexity, enabling seamless coordination between global path guidance and local trajectory generation. In the local planning layer, a fuzzy logic controller is introduced to dynamically tune the weights of the DWA trajectory evaluation function, which significantly improves the robot’s dynamic obstacle avoidance capability and motion smoothness. Comparative simulation experiments verify that the proposed method not only outperforms the conventional hybrid path planning algorithm, reducing expanded nodes by 68.09% and global planning time by 52.94%, while improving dynamic obstacle avoidance success rate by 31.43% and overall navigation efficiency by 23.95%, it also achieves better comprehensive navigation performance than the widely adopted PSO-DWA comparison algorithm. The proposed framework shows superior comprehensive performance and is well suited for the indoor autonomous navigation of home service robots. Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article

Autonomous wheelchair navigation requires both reliable global guidance and safe local interaction with the environment, typically addressed using separate perception and control strategies. This paper presents a unified vision-based control framework that combines learning-based corridor following with image-based obstacle avoidance under a common visual servoing perspective. This work provides a unified interpretation of learning-based and analytical control as complementary realizations of visual servoing. A convolutional neural network (CNN) is employed to directly predict steering commands from monocular images, enabling robust corridor following without explicit feature extraction. In parallel, obstacle avoidance is formulated as an image-based visual servoing (IBVS) task, where detected obstacles are represented as image features and regulated toward safe regions. A supervisory control strategy coordinates these components by prioritizing safety-critical avoidance when necessary, while maintaining nominal navigation otherwise. The system is implemented using a single monocular camera and deployed on a low-cost embedded platform. Experimental results demonstrate that the CNN-based module maintains stable performance under challenging visual conditions, while the IBVS controller provides predictable and reliable avoidance behavior. The proposed framework highlights the complementary roles of learning-based and analytical visual servoing, offering a practical and scalable solution for assistive autonomous mobility. Full article

The demand for reliable Positioning, Navigation, and Timing (PNT) solutions is rapidly increasing due to the growing need for precision, efficiency, and safety in unmanned systems. As operations become more autonomous, the reliance on accurate and continuous PNT data becomes critical for maintaining system integrity. The Global Navigation Satellite System (GNSS), while serving as the primary global PNT service, is vulnerable to interference, jamming, and spoofing attacks. This raises serious concerns, particularly for safety-critical applications, and urgently requires resilient Alternative PNT (A-PNT) solutions. An existing worldwide infrastructure, the Distance Measuring Equipment (DME) system, is considered one of the most promising candidates for A-PNT to address GNSS vulnerabilities. Utilising the carrier phase of the DME signal enables distance measurements with centimetre-level accuracy. However, due to the pulse system nature of DME transmissions and the sparsity of phase observations, conventional carrier tracking loops such as PLLs and FLLs struggle to maintain a reliable phase lock. To address these challenges, this work proposes a zero-crossing-integrated Kalman filter-based approach to track the DME carrier signal at an irregular rate. The performance of the proposed algorithm is validated through a series of drone tests at Cranfield University, UK. The validation results demonstrate that the proposed enhanced carrier tracking approach consistently delivers stable and accurate performance. Full article

Autonomous flight of unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments critically depends on accurate and robust visual localization. To tackle the challenges of cross-view domain discrepancies and real-time high-precision matching, we propose GRiM-Net, a two-stage joint optimization visual localization network. First, a global retrieval module aggregates features and selects the most similar satellite map candidate patches from a pre-built index, efficiently narrowing the search from the global map to a local region. Next, a fine matching module performs pixel-level keypoint detection and description on the query image and candidate patches. Bidirectional matching and weighted homography estimation are then used to map the UAV image center to satellite coordinates, yielding precise geographic positions. Both modules share a backbone with domain-adaptive batch normalization, and joint optimization of global retrieval triplet loss with fine matching keypoint, descriptor, and homography reprojection losses enables synergistic enhancement of feature representations. Ablation and comparison experiments conducted on public urban cross-view benchmarks demonstrate that GRiM-Net can achieve efficient and robust geographic coordinate regression for UAVs, providing a practical localization component for broader navigation systems. Full article