Transactions of Nanjing University of Aeronautics & Astronautics
Issue 6,2025 Table of Contents
1 Coupling-Reduced Optimal Placement of Piezoelectric Actuators for Spacecraft with Flexible Telescope
2025(6):693-709.
DOI: 10.16356/j.1005-1120.2025.06.001
Abstract:
The oscillation of large space structure （LSS） can be easily induced because of its low vibration frequency. The coupling effect between LSS vibration control and attitude control can significantly reduce the overall performance of the control system， especially when the scale of flexible structure increases. This paper proposes an optimal placement method of piezoelectric stack actuators （PSAs） network which reduces the coupling effect between attitude and vibration control system. First， a spacecraft with a honeycomb-shaped telescope is designed for a resolution-critical imaging scenario. The coupling dynamics of the spacecraft is established using finite element method （FEM） and floating frame of reference formulation （FFRF）. Second， a coupling-effect-reducing optimal placement criterion for PSAs based on coupling-matrix enhanced Gramian is designed to reduce the coupling effect excitation while balancing controllability. Additionally， a laddered multi-layered optimizing scheme is established to increase the speed and accuracy when solving the gigantic discrete optimization problem. Finally， the effectiveness of the proposed method is illustrated through numerical simulation.
The oscillation of large space structure （LSS） can be easily induced because of its low vibration frequency. The coupling effect between LSS vibration control and attitude control can significantly reduce the overall performance of the control system， especially when the scale of flexible structure increases. This paper proposes an optimal placement method of piezoelectric stack actuators （PSAs） network which reduces the coupling effect between attitude and vibration control system. First， a spacecraft with a honeycomb-shaped telescope is designed for a resolution-critical imaging scenario. The coupling dynamics of the spacecraft is established using finite element method （FEM） and floating frame of reference formulation （FFRF）. Second， a coupling-effect-reducing optimal placement criterion for PSAs based on coupling-matrix enhanced Gramian is designed to reduce the coupling effect excitation while balancing controllability. Additionally， a laddered multi-layered optimizing scheme is established to increase the speed and accuracy when solving the gigantic discrete optimization problem. Finally， the effectiveness of the proposed method is illustrated through numerical simulation.
2025(6):710-727.
DOI: 10.16356/j.1005-1120.2025.06.002
Abstract:
Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide. This paper proposes a vision Transformer （ViT） hybrid model that leverages aurora images to predict local geomagnetic station component， breaking the spatial limitations of geomagnetic stations. Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data. Essentially， the model comprises a visual geometry group （VGG） image feature extraction network， a ViT-based encoder network， and a regression prediction network. Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features. Specifically， the hybrid model achieves a 39.1% reduction in root mean square error compared to the VGG model， a 29.5% reduction compared to the ViT model and a 35.3% reduction relative to the residual network （ResNet） model. Moreover， the fitting accuracy of the model surpasses that of the VGG， ViT， and ResNet models by 2.14% 1.58%， and 4.1%， respectively.
Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide. This paper proposes a vision Transformer （ViT） hybrid model that leverages aurora images to predict local geomagnetic station component， breaking the spatial limitations of geomagnetic stations. Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data. Essentially， the model comprises a visual geometry group （VGG） image feature extraction network， a ViT-based encoder network， and a regression prediction network. Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features. Specifically， the hybrid model achieves a 39.1% reduction in root mean square error compared to the VGG model， a 29.5% reduction compared to the ViT model and a 35.3% reduction relative to the residual network （ResNet） model. Moreover， the fitting accuracy of the model surpasses that of the VGG， ViT， and ResNet models by 2.14% 1.58%， and 4.1%， respectively.
2025(6):728-748.
DOI: 10.16356/j.1005-1120.2025.06.003
Abstract:
Change detection （CD） plays a crucial role in numerous fields， where both convolutional neural networks （CNNs） and Transformers have demonstrated exceptional performance in CD tasks. However， CNNs suffer from limited receptive fields， hindering their ability to capture global features， while Transformers are constrained by high computational complexity. Recently， Mamba architecture， which is based on state space models（SSMs）， has shown powerful global modeling capabilities while achieving linear computational complexity. Although some researchers have incorporated Mamba into CD tasks， the existing Mamba-based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images， leading to limitations in extracting change features. To address these issues， we propose a novel Mamba-based CD method termed difference feature fusion Mamba model （DFFMamba） by mitigating the loss of feature locality caused by traditional Mamba-style scanning. Specifically， two distinct difference feature extraction modules are designed： Difference Mamba （DMamba） and local difference Mamba （LDMamba）， where DMamba extracts difference features by calculating the difference in coefficient matrices between the state-space equations of the bi-temporal features. Building upon DMamba， LDMamba combines a locally adaptive state-space scanning （LASS） strategy to enhance feature locality so as to accurately extract difference features. Additionally， a fusion Mamba （FMamba） module is proposed， which employs a spatial-channel token modeling SSM （SCTMS） unit to integrate multi-dimensional spatio-temporal interactions of change features， thereby capturing their dependencies across both spatial and channel dimensions. To verify the effectiveness of the proposed DFFMamba， extensive experiments are conducted on three datasets of WHU-CD， LEVIR-CD， and CLCD. The results demonstrate that DFFMamba significantly outperforms state-of-the-art CD methods， achieving intersection over union （IoU） scores of 90.67%， 85.04%， and 66.56% on the three datasets， respectively.
Change detection （CD） plays a crucial role in numerous fields， where both convolutional neural networks （CNNs） and Transformers have demonstrated exceptional performance in CD tasks. However， CNNs suffer from limited receptive fields， hindering their ability to capture global features， while Transformers are constrained by high computational complexity. Recently， Mamba architecture， which is based on state space models（SSMs）， has shown powerful global modeling capabilities while achieving linear computational complexity. Although some researchers have incorporated Mamba into CD tasks， the existing Mamba-based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images， leading to limitations in extracting change features. To address these issues， we propose a novel Mamba-based CD method termed difference feature fusion Mamba model （DFFMamba） by mitigating the loss of feature locality caused by traditional Mamba-style scanning. Specifically， two distinct difference feature extraction modules are designed： Difference Mamba （DMamba） and local difference Mamba （LDMamba）， where DMamba extracts difference features by calculating the difference in coefficient matrices between the state-space equations of the bi-temporal features. Building upon DMamba， LDMamba combines a locally adaptive state-space scanning （LASS） strategy to enhance feature locality so as to accurately extract difference features. Additionally， a fusion Mamba （FMamba） module is proposed， which employs a spatial-channel token modeling SSM （SCTMS） unit to integrate multi-dimensional spatio-temporal interactions of change features， thereby capturing their dependencies across both spatial and channel dimensions. To verify the effectiveness of the proposed DFFMamba， extensive experiments are conducted on three datasets of WHU-CD， LEVIR-CD， and CLCD. The results demonstrate that DFFMamba significantly outperforms state-of-the-art CD methods， achieving intersection over union （IoU） scores of 90.67%， 85.04%， and 66.56% on the three datasets， respectively.
2025(6):749-766.
DOI: 10.16356/j.1005-1120.2025.06.004
Abstract:
Cracks represent a significant hazard to pavement integrity， making their efficient and automated extraction essential for effective road health monitoring and maintenance. In response to this challenge， we propose a crack automatic extraction network model that integrates multi-scale image features， thereby enhancing the model’s capability to capture crack characteristics and adaptation to complex scenarios. This model is based on the ResUNet architecture， makes modification to the convolutional layer of the model， proposes to construct multiple branches utilizing different convolution kernel sizes， and adds a atrous spatial pyramid pooling module within the intermediate layers. In this paper， comparative experiments on the performance of the basic model， ablation experiments， comparative experiments before and after data augmentation， and generalization verification experiments are conducted. Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges. The overall performance of the model， as measured by the F1-score， reaches 71.03%， reflecting a 2.1% improvement over the conventional ResUNet.
Cracks represent a significant hazard to pavement integrity， making their efficient and automated extraction essential for effective road health monitoring and maintenance. In response to this challenge， we propose a crack automatic extraction network model that integrates multi-scale image features， thereby enhancing the model’s capability to capture crack characteristics and adaptation to complex scenarios. This model is based on the ResUNet architecture， makes modification to the convolutional layer of the model， proposes to construct multiple branches utilizing different convolution kernel sizes， and adds a atrous spatial pyramid pooling module within the intermediate layers. In this paper， comparative experiments on the performance of the basic model， ablation experiments， comparative experiments before and after data augmentation， and generalization verification experiments are conducted. Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges. The overall performance of the model， as measured by the F1-score， reaches 71.03%， reflecting a 2.1% improvement over the conventional ResUNet.
2025(6):767-788.
DOI: 10.16356/j.1005-1120.2025.06.005
Abstract:
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route， lacking alternative paths and posing challenges in responding to emergencies， this study employs the “quantile-inflection point method” to analyze specific deviation trajectories， determine deviation thresholds， and identify commonly used deviation paths. By combining multiple similarity metrics， including Euclidean distance， Hausdorff distance， and sector edit distance， with the density-based spatial clustering of applications with noise （DBSCAN） algorithm， the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs. A case study of 23 578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework. Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances， with higher silhouette coefficients and lower Davies-Bouldin indices， ensuring better intra-cluster compactness and inter-cluster separation. Based on clustering results， 19 representative trajectory options are identified， covering both nominal and deviation paths， which significantly enhance route diversity and reflect actual flight practices. This provides a practical basis for optimizing flight paths and scheduling， enhancing the flexibility of route selection for flights between city pairs.
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route， lacking alternative paths and posing challenges in responding to emergencies， this study employs the “quantile-inflection point method” to analyze specific deviation trajectories， determine deviation thresholds， and identify commonly used deviation paths. By combining multiple similarity metrics， including Euclidean distance， Hausdorff distance， and sector edit distance， with the density-based spatial clustering of applications with noise （DBSCAN） algorithm， the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs. A case study of 23 578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework. Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances， with higher silhouette coefficients and lower Davies-Bouldin indices， ensuring better intra-cluster compactness and inter-cluster separation. Based on clustering results， 19 representative trajectory options are identified， covering both nominal and deviation paths， which significantly enhance route diversity and reflect actual flight practices. This provides a practical basis for optimizing flight paths and scheduling， enhancing the flexibility of route selection for flights between city pairs.
2025(6):789-800.
DOI: 10.16356/j.1005-1120.2025.06.006
Abstract:
This study discusses a machine learning-driven methodology for optimizing the aerodynamic performance of both conventional， like common research model （CRM）， and non-conventional， like Bionica box-wing， aircraft configurations. The approach leverages advanced parameterization techniques， such as class and shape transformation （CST） and Bezier curves， to reduce design complexity while preserving flexibility. Computational fluid dynamics（CFD） simulations are performed to generate a comprehensive dataset， which is used to train an extreme gradient boosting （XGBoost） model for predicting aerodynamic performance. The optimization process， using the non-dominated sorting genetic algorithm （NSGA-Ⅱ）， results in a 12.3% reduction in drag for the CRM wing and an 18% improvement in the lift-to-drag ratio for the Bionica box-wing. These findings validate the efficacy of machine learning based method in aerodynamic optimization， demonstrating significant efficiency gains across both configurations.
This study discusses a machine learning-driven methodology for optimizing the aerodynamic performance of both conventional， like common research model （CRM）， and non-conventional， like Bionica box-wing， aircraft configurations. The approach leverages advanced parameterization techniques， such as class and shape transformation （CST） and Bezier curves， to reduce design complexity while preserving flexibility. Computational fluid dynamics（CFD） simulations are performed to generate a comprehensive dataset， which is used to train an extreme gradient boosting （XGBoost） model for predicting aerodynamic performance. The optimization process， using the non-dominated sorting genetic algorithm （NSGA-Ⅱ）， results in a 12.3% reduction in drag for the CRM wing and an 18% improvement in the lift-to-drag ratio for the Bionica box-wing. These findings validate the efficacy of machine learning based method in aerodynamic optimization， demonstrating significant efficiency gains across both configurations.
2025(6):801-817.
DOI: 10.16356/j.1005-1120.2025.06.007
Abstract:
The study of the spallation of thermal barrier coatings on turbine blades and its influence is of great significance for gas turbine safety operation. However， numerical simulation related to thermal barrier coatings is difficult and time-costly， which makes it hard to meet engineering demands. Therefore， this work establishes a rapid prediction model for the surface temperature and cooling efficiency of turbine blades with localized spallation of thermal barrier coatings based on a thin-wall thermal resistance model. Firstly， the influence of localized spallation of thermal barrier coatings on the cooling efficiency of typical turbine blades is numerically investigated. Then， based on the simulation data set and multi-layer perception （MLP） neural network， an intelligent prediction model for the temperature and cooling efficiency distribution of localized spallation of coatings is constructed， which can rapidly predict the surface temperature and cooling efficiency of the blade under the situation of spallation of coating at any position on the blade surface. The results show that， under a certain spallation area， the shape of localized coating spallation has little influence on the cooling efficiency， while the increase of spallation thickness will cause a linear increase in the average temperature of the blade surface. The prediction error of the proposed rapid prediction model for the average surface temperature and cooling efficiency of blades is within 2%， and the prediction error of the temperature and cooling efficiency at the spallation position is within 6% for 80% of the samples， with an overall average error within 10%. It is concluded from the rapid prediction model that when the depth of coating spallation increases， the closer the spallation position is to the leading edge of the blade， the greater the difference in cooling efficiency is， and the degree of influence of coating spallation on the cooling efficiency also increases.
The study of the spallation of thermal barrier coatings on turbine blades and its influence is of great significance for gas turbine safety operation. However， numerical simulation related to thermal barrier coatings is difficult and time-costly， which makes it hard to meet engineering demands. Therefore， this work establishes a rapid prediction model for the surface temperature and cooling efficiency of turbine blades with localized spallation of thermal barrier coatings based on a thin-wall thermal resistance model. Firstly， the influence of localized spallation of thermal barrier coatings on the cooling efficiency of typical turbine blades is numerically investigated. Then， based on the simulation data set and multi-layer perception （MLP） neural network， an intelligent prediction model for the temperature and cooling efficiency distribution of localized spallation of coatings is constructed， which can rapidly predict the surface temperature and cooling efficiency of the blade under the situation of spallation of coating at any position on the blade surface. The results show that， under a certain spallation area， the shape of localized coating spallation has little influence on the cooling efficiency， while the increase of spallation thickness will cause a linear increase in the average temperature of the blade surface. The prediction error of the proposed rapid prediction model for the average surface temperature and cooling efficiency of blades is within 2%， and the prediction error of the temperature and cooling efficiency at the spallation position is within 6% for 80% of the samples， with an overall average error within 10%. It is concluded from the rapid prediction model that when the depth of coating spallation increases， the closer the spallation position is to the leading edge of the blade， the greater the difference in cooling efficiency is， and the degree of influence of coating spallation on the cooling efficiency also increases.
2025(6):818-828.
DOI: 10.16356/j.1005-1120.2025.06.008
Abstract:
This study investigates the interfacial adhesion enhancement mechanisms of TiAlN coatings deposited on nitrocarburized 300M ultra-high-strength steel substrates. Through radio frequency （RF） magnetron sputtering technology， TiAlN coatings （approximately 4 μm thick） are fabricated on both pristine and plasma-nitrocarburized （PNC） substrates. Comparative analyses of phase composition， microstructure， and mechanical properties are conducted using field emission scanning electron microscope （FESEM）， X-ray diffraction（XRD）， nanoindentation， and scratch testing. Molecular dynamics （MD） simulations with Materials Studio （MS） software elucidate atomic-scale interactions between TiAlN coatings and substrates. Results demonstrate that the PNC pretreatment generates a dual-phase structure （about 65 μm thick） comprising the γ-Fe4N compound layer and a high-hardness diffusion layer， establishing a continuous hardness gradient at the coating-substrate interface. The PNC/TiAlN composite coating exhibits enhanced interfacial adhesion strength， attributed to mechanical interlocking from plasma-etched microvoids and optimized lattice matching. Scratch tests reveal a significant increase in critical load to 60 N for coating delamination in PNC/TiAlN systems compared with monolayer coatings. These improvements mitigate brittle spallation risks while maintaining superior hardness （29.26 GPa） and wear resistance. This paper provides atomic-level insights into adhesion enhancement mechanisms and proposes a viable duplex surface engineering strategy for high-strength steel components.
This study investigates the interfacial adhesion enhancement mechanisms of TiAlN coatings deposited on nitrocarburized 300M ultra-high-strength steel substrates. Through radio frequency （RF） magnetron sputtering technology， TiAlN coatings （approximately 4 μm thick） are fabricated on both pristine and plasma-nitrocarburized （PNC） substrates. Comparative analyses of phase composition， microstructure， and mechanical properties are conducted using field emission scanning electron microscope （FESEM）， X-ray diffraction（XRD）， nanoindentation， and scratch testing. Molecular dynamics （MD） simulations with Materials Studio （MS） software elucidate atomic-scale interactions between TiAlN coatings and substrates. Results demonstrate that the PNC pretreatment generates a dual-phase structure （about 65 μm thick） comprising the γ-Fe4N compound layer and a high-hardness diffusion layer， establishing a continuous hardness gradient at the coating-substrate interface. The PNC/TiAlN composite coating exhibits enhanced interfacial adhesion strength， attributed to mechanical interlocking from plasma-etched microvoids and optimized lattice matching. Scratch tests reveal a significant increase in critical load to 60 N for coating delamination in PNC/TiAlN systems compared with monolayer coatings. These improvements mitigate brittle spallation risks while maintaining superior hardness （29.26 GPa） and wear resistance. This paper provides atomic-level insights into adhesion enhancement mechanisms and proposes a viable duplex surface engineering strategy for high-strength steel components.
2025(6):829-840.
DOI: 10.16356/j.1005-1120.2025.06.009
Abstract:
Software systems play increasing important roles in modern society， and the ability against attacks is of great practical importance to crucial software systems， resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years. In this paper， based on the source code of Tar and MySQL， we propose an approach to generate coupled software networks and construct three kinds of directed software networks： The function call network， the weakly coupled network and the strongly coupled network. The structural properties of these complex networks are extensively investigated. It is found that the average influence and the average dependence for all functions are the same. Moreover， eight attacking strategies and two robustness indicators （the weakly connected indicator and the strongly connected indicator） are introduced to analyze the robustness of software networks. This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one. For MySQL， high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used. On the other hand， high out-degree strategy is a good choice when the strongly connected indicator is adopted. This work will highlight a better understanding of the structure and robustness of software networks.
Software systems play increasing important roles in modern society， and the ability against attacks is of great practical importance to crucial software systems， resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years. In this paper， based on the source code of Tar and MySQL， we propose an approach to generate coupled software networks and construct three kinds of directed software networks： The function call network， the weakly coupled network and the strongly coupled network. The structural properties of these complex networks are extensively investigated. It is found that the average influence and the average dependence for all functions are the same. Moreover， eight attacking strategies and two robustness indicators （the weakly connected indicator and the strongly connected indicator） are introduced to analyze the robustness of software networks. This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one. For MySQL， high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used. On the other hand， high out-degree strategy is a good choice when the strongly connected indicator is adopted. This work will highlight a better understanding of the structure and robustness of software networks.
2025(6):841-851.
DOI: 10.16356/j.1005-1120.2025.06.010
Abstract:
In traditional meander line coil electromagnetic acoustic transducer （MLC-EMAT） structures， the bias magnetic field is usually set to be along the normal direction of plate surface. However， since the particle vibration of the antisymmetric Lamb wave is always dominated by out-of-plane components， using bias magnetic field perpendicular to plate surface is kind of inefficient. In this paper， the performance of both the normal bias magnetic field EMAT （NB-EMAT） and the parallel bias magnetic field EMAT （PB-EMAT） for transmitting and receiving A0 mode Lamb waves are thoroughly studied. The mechanisms of these two structures are elaborated. First， the finite element models of both structures are established. The magnetic fields of these two EMATs are numerically calculated and the results are compared with experiments. Then， the Lorentz force distributions excited by the two EMATs are compared to prove the feasibility of improving the excitation efficiency of MLC-EMAT by selecting the direction of bias magnetic field. Furthermore， the excitation efficiencies of NB-EMAT and PB-EMAT are quantitatively analyzed and compared in simulation software. Results show that the excitation efficiency of PB-EMAT is 108% higher than NB-EMAT. Finally， several groups of comparative experiments are conducted to verify the conclusion obtained through numerical calculation. Experimental results show that by simply replacing the tradition NB-EMAT with PB-EMAT， the excitation efficiency can be greatly increased by more than 50%. If PB-EMATs are used as both the receiver and transmitter， the excitation efficiency can be further increased by 113%.
In traditional meander line coil electromagnetic acoustic transducer （MLC-EMAT） structures， the bias magnetic field is usually set to be along the normal direction of plate surface. However， since the particle vibration of the antisymmetric Lamb wave is always dominated by out-of-plane components， using bias magnetic field perpendicular to plate surface is kind of inefficient. In this paper， the performance of both the normal bias magnetic field EMAT （NB-EMAT） and the parallel bias magnetic field EMAT （PB-EMAT） for transmitting and receiving A0 mode Lamb waves are thoroughly studied. The mechanisms of these two structures are elaborated. First， the finite element models of both structures are established. The magnetic fields of these two EMATs are numerically calculated and the results are compared with experiments. Then， the Lorentz force distributions excited by the two EMATs are compared to prove the feasibility of improving the excitation efficiency of MLC-EMAT by selecting the direction of bias magnetic field. Furthermore， the excitation efficiencies of NB-EMAT and PB-EMAT are quantitatively analyzed and compared in simulation software. Results show that the excitation efficiency of PB-EMAT is 108% higher than NB-EMAT. Finally， several groups of comparative experiments are conducted to verify the conclusion obtained through numerical calculation. Experimental results show that by simply replacing the tradition NB-EMAT with PB-EMAT， the excitation efficiency can be greatly increased by more than 50%. If PB-EMATs are used as both the receiver and transmitter， the excitation efficiency can be further increased by 113%.
2025(6):852-864.
DOI: 10.16356/j.1005-1120.2025.06.011
Abstract:
With the widespread adoption of unmanned aerial vehicle （UAV） technology， task scheduling for UAV swarms has become a crucial approach to improve operational efficiency. Most existing studies oversimplify the operational process rules of UAVs， making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints. To address this limitation， this paper proposes a UAV swarm task scheduling problem with limited communication range （UAVS-LCR） and establishes an integer programming model for its formal description. For solving this problem， a multi-neighborhood iterative local search （MNILS） algorithm is designed， which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations. This algorithm generates high-quality initial solutions via a greedy construction strategy， combines insertion search， multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods， and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity. This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations. Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm， the artificial bee colony algorithm， the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.
With the widespread adoption of unmanned aerial vehicle （UAV） technology， task scheduling for UAV swarms has become a crucial approach to improve operational efficiency. Most existing studies oversimplify the operational process rules of UAVs， making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints. To address this limitation， this paper proposes a UAV swarm task scheduling problem with limited communication range （UAVS-LCR） and establishes an integer programming model for its formal description. For solving this problem， a multi-neighborhood iterative local search （MNILS） algorithm is designed， which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations. This algorithm generates high-quality initial solutions via a greedy construction strategy， combines insertion search， multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods， and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity. This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations. Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm， the artificial bee colony algorithm， the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.
2025(6):865-874.
DOI: 10.16356/j.1005-1120.2025.06.012
Abstract:
In this paper， an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter （ASTUKF） is advanced to improve the precision of pose estimation and the stability for data computation. To suppress high-frequency noise， an infinite impulse response filter （IIRF） is introduced at the front end of ASTUKF to preprocess the original data. Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering. After that， the data from the experiment were tested on the hardware experiment platform. The experimental results show that compared to the traditional extended Kalman filter （EKF） and unscented Kalman filter （UKF） algorithms， the root mean square error （RMSE） of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5% and 36.1%； the RMSE of the pitch axis results decreases by nearly 58.4% and 51.5%， respectively； and the RMSE of the yaw axis results decreases almost 62.8% and 50.9%， correspondingly. The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.
In this paper， an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter （ASTUKF） is advanced to improve the precision of pose estimation and the stability for data computation. To suppress high-frequency noise， an infinite impulse response filter （IIRF） is introduced at the front end of ASTUKF to preprocess the original data. Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering. After that， the data from the experiment were tested on the hardware experiment platform. The experimental results show that compared to the traditional extended Kalman filter （EKF） and unscented Kalman filter （UKF） algorithms， the root mean square error （RMSE） of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5% and 36.1%； the RMSE of the pitch axis results decreases by nearly 58.4% and 51.5%， respectively； and the RMSE of the yaw axis results decreases almost 62.8% and 50.9%， correspondingly. The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.
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