Pipe corrosion, specifically pitting corrosion, is the main cause of destructive pipe leakage, driven by the harsh working environment of liquid and gas transportation. Therefore, detecting pitting corrosion is essential for ensuring the safe operation of metal pipes. This study investigates a nonlinear ultrasonic technique using macro fiber composite transducers, aiming to assess pitting corrosion in metal pipes at an early stage, with a focus on characterizing the influence of temperature on the ultrasonic nonlinearity. Macro fiber composite transducers with flexibility and high ultrasonic performance were used to actuate and detect ultrasonic guided waves propagating in pipes with curved surfaces. Considering the multi-mode propagation characteristics, the 1.4 MHz second-harmonic ultrasonic component generated by the nonlinear interaction between ultrasonic guided waves and pitting corrosion was extracted. Repeated experiments revealed that both the second-harmonic amplitude and relative nonlinear parameter exhibited a monotonic increase with the number of cycles and area of pitting corrosion. For comparison with the nonlinear results, statistical metrics including the mean slopes, coefficient of determination, and relative standard deviation of the linear fitting parameter were determined alongside the linear ultrasonic experiments. These results indicate that, despite some inherent data variability, the proposed nonlinear ultrasonic technique exhibits comparatively better sensitivity, goodness-of-fit and repeatability than linear ultrasonic methods for identifying pitting corrosion. Thus, the proposed nonlinear ultrasonic technique using macro fiber composites offers a promising complementary alternative for early corrosion assessment in metal pipes.

In response to the growing population of resident space object (RSO) in the low Earth orbit (LEO), a novel celestial navigation approach that is based on RSO observations is proposed in this paper. This study first analyzes the feasibility of navigation through RSO observations. Subsequently, an autonomous position and attitude determination algorithm is derived on the basis of RSO observations, and an inertial navigation system and RSO integrated navigation model is established. By employing factor graph optimization, the method effectively combines RSO-based navigation with inertial navigation. Finally, the navigation algorithm is verified through simulations using ephemeris data combined with star catalogs of different scales. The simulation results demonstrate that the INS/RSO integrated navigation system can achieve autonomous navigation. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper presents a cluster-optimized collaborative navigation method for large-scale multi-layer swarm systems, addressing challenges in global navigation satellite system (GNSS)-denied environments. The proposed approach divides the swarm into adaptive clusters, selecting cluster heads (CHs) based on optimized metrics to reduce communication load while maintaining navigation accuracy; it integrates a time difference of arrival (TDOA)/frequency difference of arrival (FDOA)-based collaborative navigation model for heterogeneous node coordination and employs a multi-dimensional scaling (MDS) algorithm for intra-cluster relative positioning and coordinate alignment. The system architecture features a three-layer structure consisting of reference nodes, intermediary nodes, and clustered nodes, enabling efficient resource utilization and robust navigation support. Experimental results demonstrate that the method substantially lowers the load while keeping a satisfactory navigation accuracy, facilitating reliable navigation in complex scenarios such as disaster response and intelligent logistics. This work provides theoretical and technical support for enhancing the adaptability and operational efficiency of large-scale multi-layer swarm systems.

Composite stiffened panels utilizing secondary bonding are widely employed in aviation industry. However, the bond strength is critically dependent on the degree of cure (DoC) of the epoxy adhesive film, necessitating in-situ monitoring. Although ultrasonic guided wave techniques enable in-situ monitoring, the complex geometries of panels attenuate the ultrasonic waves and complicate wave propagation mechanisms, posing significant challenges for monitoring. This study proposed an acousto-ultrasonic (AU) method using a highly sensitive cantilever microfiber Bragg grating (CMFBG) as a sensor and macro-fiber composite as an actuator to investigate wave propagation behavior and achieve DoC monitoring for epoxy adhesive films in composite stiffened panels. The CMFBG-based AU method successfully monitors the guided wave and extracts time-of-flight (ToF) during the secondary bonding process. Finite element simulation demonstrates that variations in mechanical properties induced by DoC evolution can be reflected through ToF of ultrasonic wave when actuator and sensor are respectively bonded on the skin and stiffener at the two ends of the composite panel. The experimental and simulated ToF shows strong correlations with DoC and storage modulus, which are estimated by differential scanning calorimeter and dynamic mechanical analysis testing, respectively, confirming that the proposed CMFBG-based AU method enables effective DoC monitoring of secondary bonded composite stiffened panels.

Simultaneous Localization and Mapping (SLAM) has always been a hot topic in the fields of intelligent industry and mobile robotics. To effectively limit drift caused by large-scale operation, we can introduce sensors that offer absolute measurements, such as GNSS. However, GNSS will have no signal, resulting in large positioning errors, due to challenging environments such as occlusion and shielding. Thus, we proposed a landmark-based multi-sensor fusion SLAM algorithm to solve the question of carrier’s location in GNSS-denied. During GNSS-denied, we use relative information between landmarks and carrier to constraint the poses of carrier, thereby effectively eliminating pose drift and achieving high-precision autonomous positioning. Additionally, we consider three elements, landmarks distribution, the number of landmarks and the style of relative information, may influence the performance of proposed algorithm, we conducted multiple experiments in two-dimensional (2-D) and three-dimensional (3-D) spaces to verify its impact on pose estimation.

Large-scale UAV swarms can adapt to various task environments and requirements, enhancing operational capabilities. In a traditional full-connected cooperative strategy, all reference UAVs and assisted UAVs are interconnected, resulting in complex cooperative network structures, significant resource waste, and inability to ensure real-time navigation and positioning. Therefore, this article proposes a game-connected cooperative strategy. A game graph model is established, and a game-based network formation strategy for cooperative navigation is proposed, allowing UAVs to autonomously select cooperative connections. Simulation results demonstrate that the proposed algorithm simplifies the UAV network structure while effectively ensuring swarm navigation and positioning accuracy. It significantly improves computational efficiency for swarm navigation and is of great importance for enhancing the adaptability and real-time performance of large-scale UAV navigation.

This paper proposes a probabilistic hierarchical cooperative architecture to address uneven navigation capabilities and rigid structures in UAV swarms under GNSS-denied conditions. By combining GDOP and navigation error, a continuous probability model is built to drive role assignment and connection regulation. Unlike fixed-role schemes, the proposed method enables adaptive cooperation and flexible behavior adjustment, improving robustness and self-organization for swarm navigation in complex environments.

To address the challenges of collaborative navigation for cluster systems within cross-domain scenarios, a novel adaptive clustering method of collaborative navigation for cross-domain swarm system is proposed. By partitioning cross-domain clusters into distinct spatial domains, an adaptive clustering method for cross-domain systems, which combines centralized and distributed clustering algorithms, is designed to optimize clustering problems for large-scale cluster system in low-altitude domain. Targeting inter-domain as well as intra-cluster navigation scenarios, specific collaborative navigation models are formulated based on the relative distance measurement between cluster nodes, which enhances collaborative navigation performance for cluster nodes in low-altitude domain. Simulation results demonstrate that the proposed scheme outperforms traditional collaborative navigation methods, which improves the performance of collaborative navigation for cluster systems in cross-domain. Furthermore, its resilience to environmental challenges in low-altitude domain underscores its significant practical utility and contributions.

The fuel economy of plug-in hybrid electric vehicles (PHEVs) is strongly affected by the battery state of charge (SOC) depletion pattern. This paper proposes and studies a real-time traffic-based SOC reference planning method. The method uses a dataset to collect and capture real traffic information and then enriches the dataset using a data augmentation method developed in this paper. The augmented dataset is optimized by dynamic programing (DP) algorithm to obtain the optimal reference SOC for model training. The traffic information and optimal reference SOC are processed and used to train a long-short term memory (LSTM) neural network, which is used for online reference SOC planning. Finally, a predictive energy management (PEM) strategy is adopted to follow the SOC reference by optimizing instantaneous power allocation with the predicted velocities. Simulation results show that the proposed method outperforms the linear reference SOC planning method in both smooth and congested traffic scenarios.

To address the issue of traditional factor graph methods being unable to handle the dynamic change in sensor measurement accuracy during the operational process, an adaptive weight function is introduced and an improved factor graph method based on adaptive weight is proposed. By calculating the residual between the predicted value of inertial preintegration and the measured value of auxiliary sensors in real-time, the fusion information weight of the corresponding factor nodes are dynamically adjusted. Compared with traditional factor graph algorithms, this method can improve the optimization accuracy and robustness of factor graph algorithms in the situation of step faults, gradual faults, or rejection faults in auxiliary sensors. The simulation experimental results show that when the auxiliary sensor produces measurement faults, compared with traditional factor graph method, the improved factor graph method based on adaptive weights has higher robustness and accuracy. When measurement faults occur in auxiliary sensors, its position, velocity, and attitude estimation accuracy RMSE values have been improved by more than 45%.

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEVs). A key technology in PEM is velocity prediction and its accuracy greatly affects the effectiveness of a PEM strategy. This paper proposes a novel dynamic competitive velocity prediction method based on Markov state space (SS) reconstruction. The basic Markov model is introduced and its performance is fully evaluated. The Markov SS is designed by the K-Means++ clustering method to support online reconstruction. The transition probability matrix (TPM) is updated to adapt to the actual driving scenario. The dynamic competitive prediction method combines the basic and the reconstructed Markov models to achieve better performance. The velocity prediction performance is validated through repetitive complex driving conditions. Simulation result shows that the proposed method has superior performance in both prediction accuracy and computing time. For the complex driving condition scenario, the proposed method can reduce prediction error by 5.7%–9.1% comparing to the basic Markov model and its computing time is about 1% of that of LSTM when the prediction horizon is 5 s.

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

Precise navigation is the key to guaranteeing the mission execution of unmanned aerial vehicle (UAV) swarms. Cooperative navigation (CN) realized through information interaction between UAVs can enhance the navigation performance of UAVs in complex environments. In this article, we construct a distributed CN framework that can fuse the measurements from various onboard navigation sensors and inter-UAV ranging based on factor graph (FG) and belief propagation (BP). In view of the computational efficiency, we propose a simplified Gaussian particle filter (GPF) for message passing and belief calculation, which reduces the computational load. The proposed algorithm is tested and verified using Monte Carlo simulations and flight test. The simulation results show that with similar positioning performance, the average processing time of the proposed algorithm is reduced by 88% compared to the sum-product algorithm for wireless network (SPAWN) algorithm.

In light of the satellite rejection environment and how aircraft can obtain high-precision positioning, this paper proposes a collaborative correction algorithm for aircraft based on the rank-defect network. Aiming at the problem of insufficient anchor points, which result in insufficient observations and the divergence of aircraft inertial navigation errors, this algorithm can effectively improve the navigation performance of cluster aircraft. On the basis of the observation information provided by the anchor aircraft, the observation information between aircraft is fully utilized to improve the observability of the aircraft cluster positioning method. At the same time, the pseudo-observation equation of heterogeneous aircraft cluster positioning is introduced, and the divergence of inertial navigation positioning errors caused by insufficient observations is suppressed by the pseudo-observation solution. On the basis of introducing the pseudo-observation equation, the inertial navigation error is solved and corrected by the Newton iterative method and the divergence of the inertial navigation position error is restrained. Compared with an aircraft cluster positioning method that does not use the inertial navigation error co-correction based on the pseudo-observation solution, this paper can achieve better overall cluster positioning accuracy when the available observations are insufficient, which is suitable for practical applications.

In recent years, multi-UAV collaborative cluster technology has attracted more and more attention from domestic and foreign researchers, and multi-UAV collaborative navigation is a crucial part of cluster collaborative technology. In many complex environments, GNSS signal rejection may occur, making it difficult for UAVs to achieve precise positioning and navigation solely relying on inertial navigation. In this case, multi-UAV collaboration can effectively suppress the divergence of UAV inertial navigation errors. However, considering factors such as communication range and capacity, electromagnetic interference, and sudden faults, sometimes the collaborative connection of UAVs is constrained. This article analyzes the collaborative connection strategies of UAV clusters under the premise of limited collaborative connections, proposes three collaborative connection strategies, namely RS, RL, RH, and proposes a “RLH” collaborative connection optimization method that alternates the use of “RL” and “RH” strategies, which improves and stabilizes the optimized collaborative navigation and positioning performance.

The collaboration among swarmed aircraft can provide additional observation to improve the integrity of its onboard navigation systems. But due to the limitation on relative communicating and measuring burdens, adopting all the observations between aircraft in the swarm is inefficient. The geometry of the collaborated partners is a key factor that influences the effectiveness of the navigation integrity augmentation, which needs to be optimized in collaborative integrity augmented navigation. In this paper, the integrity augmented navigation method for aerial swarm based on collaborative partner optimization is proposed. The geometry constructed by the GNSS satellites and the cooperative partners in the aerial swarm are analyzed dynamically, and the augmented integrity protection levels with different collaborative relationships are predicted to distinguish the partner essential for navigation integrity augmentation. Then the collaborative partner makes a key contribution to integrity augmentation adopted in collaborative navigation integrity monitoring, improving the efficiency of the collaborative navigation. The simulation results indicate the effectiveness of the proposed cooperative partnership optimization strategy, as well as the superiority of the proposed method compared with the traditional independent integrity framework in improving the integrity protection level and fault detection capacity.

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEV). A Markov velocity predictor optimization method and its applications in PHEV energy management is studied in this paper. The initial Markov velocity predictor is constructed using complete driving cycle information and the state space of the Markov velocity predictor is then optimized for specified driving conditions using simulated annealing algorithm (SAA). The practical driving conditions are identified using a multi-feature driving condition recognition unit by using the support vector machine (SVM) method. Based on the driving conditions identified, velocities are predicted using the proposed method and optimized using dynamic programming (DP) algorithm in conjunction with the state of charge (SOC) reference and vehicle state. The energy management strategy derived is then implemented in the vehicle controllers. Comparing with the traditional rule-based energy management strategy, simulation results indicate that the PEM strategy proposed herein can reduce fuel consumption.

The geometry of the collaborating partners is one of the factors that influence the effectiveness of collaborative resilient navigation. In this chapter, an improved geometric dilution of precision is introduced to quantitatively evaluate geometric configurations in collaborative resilient navigation fusion. The influence of geometry on the accuracy of collaborative resilient navigation fusion is discussed in both the GNSS-augmented and GNSS-denied situations. Geometry optimization algorithms based on a geometric analysis method and an algebraic search method are proposed, with simulated examples.

Cooperation between UAVs can significantly improve overall positioning accuracy of unmanned aerial swarm, which is of great importance to swarm navigation. In complex terrain environment, positioning signals from navigation satellites and partner UAVs are often blocked, resulting in mixed presence of relative range and angle observation, causing serious reduction in positioning accuracy of some UAVs. In order to improve their positioning accuracy, this paper proposes a cooperative navigation enhancement method based on hybrid linearization belief propagation, which utilizes hybrid relative observations of range and range/angle between swarm UAVs. Simulation experiment is carried out in a complex-terrain environment based on 3D map to simulate signal blockage. Simulation result shows that the algorithm can utilize hybrid relative observations between swarm UAVs, has a good effect on the mitigation of inertial navigation position error, and is of great significance to navigation of swarm aircraft in GNSS-challenging environment.

In the cooperative mission of swarm aircraft, UAV, a new tool with low cost and high efficiency, is highly flexible in location technique and covers a wide area, which can improve the success rate of the execution of the mission. However, during the process of performing missions, there is the problem of low positioning accuracy and reduced efficiency of some air vehicles due to the complexity of the environment and some unexpected factors. Therefore, this paper proposes a UAV cluster cooperative navigation and positioning method based on relative distance difference, aiming to enhance the regional cooperative navigation capability and solve the problem of position loss in areas without satellite navigation signals under unexpected situations. The solution is practically feasible after the simulation and actual test verification.