Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft...Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.展开更多
In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing ...In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.展开更多
This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot ...This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.展开更多
Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were coll...Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were collected to investigate the influence of wave-induced stress on W within these wind ranges.The friction velocity was recalculated based on turbulent stress,and wind profiles were modified to account for wave-induced stress and swell presence on the sea surface.The study examined W’s relationship with multiple parameters,including friction velocity(u*),breaking wave Reynolds numbers,wavesea Reynolds numbers,and wave age.The analysis utilized both conventional u*and turbulent stress-based friction velocity(u*turb).When utilizing u*turb rather than u*,the estimation model’s fitting results revealed an increase in correlation coefficient(R2)from 0.51 to 0.62,and a decrease in root mean square error(RMSE)from 0.0652 to 0.0574.Additionally,when parameterizing W using the windsea Reynolds number,with u_(*turb) replacing u*and wind wave height substituting mixed wave height,the R^(2) increased from 0.38 to 0.53,and the RMSE decreased from 0.0737 to 0.0668.The results demonstrate that calculating u*using the turbulent stress-based method,along with wind wave height and peak wave speed of mixed waves,yields stronger correlation with W.This correlation improvement stems from the inhibition of wave breaking by swell and wave-induced stress.The integration of turbulent stress and wind wave field measurements enhances the understanding of relationships between W and various parameters.However,swell effects on wind profiles do not substantially affect W estimation using wind speed-related parameters.展开更多
Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV...Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.展开更多
As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehi...As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.展开更多
In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel ...In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.展开更多
Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in t...Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in the delivery of vaccination services.With the advent of COVID-19 pandemic,many countries have included community pharmacists and pharmacies in vaccination services to improve coverage.This study described the delivery of vaccination services in community pharmacies using the COVID-19 experience and how their involvement impacted vaccination coverage in Nigeria.It also exposed how this experience can be used to support policy revisions to formally recognize pharmacists in immunization delivery.Methods:A descriptive cross-sectional study was conducted among 474 community pharmacists in two southwestern States in Nigeria,using a semi-structured questionnaire.It determines the number of community pharmacists who have been trained in the delivery of vaccination services,the types of vaccination services provided,and vaccines administered in their pharmacies.Data were analyzed with descriptive and inferential statistics and p-value at≤0.05.Results:Response rate was 86.7%.Less than half of the respondents(40.1%)had undergone vaccination training.Of the 129(31.4%)respondents that provide vaccination services,72(55.8%)administer vaccines in their pharmacies.Out of these 72 respondents;45(62.5%)were administering vaccines before their involvement in COVID-19 vaccine administration;57(79.2%)of the health personnel who administer vaccines were pharmacists;60(83.3%)of them administer vaccines on request;22(30.6%)administered COVID-19 vaccines only;and only 7(9.7%)of the respondents had administered over 500 doses of COVID-19 vaccines.Training in vaccination was associated with the vaccination services provided(p<0.05).Respondents suggested government support through legal framework and policy review,training and empowering pharmacists in vaccine administration,and recognition of community pharmacists as PHC providers.展开更多
In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance pr...In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.展开更多
基金National Natural Science Foundation of China(62402020,62303022)Beijing Nova Program(20240484720)+1 种基金Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203043)BTBU Digital Business Platform Project byBMEC.
文摘Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.
文摘In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.
文摘This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.
基金supported by the National Natural Science Foundation of China(Grant No.42276001)2024 Qinhuangdao Social Science Development Research Project(Grant No.2024LX206)Hebei Agricultural University Research Project for Talented scholars(Grant No.YJ201835).
文摘Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were collected to investigate the influence of wave-induced stress on W within these wind ranges.The friction velocity was recalculated based on turbulent stress,and wind profiles were modified to account for wave-induced stress and swell presence on the sea surface.The study examined W’s relationship with multiple parameters,including friction velocity(u*),breaking wave Reynolds numbers,wavesea Reynolds numbers,and wave age.The analysis utilized both conventional u*and turbulent stress-based friction velocity(u*turb).When utilizing u*turb rather than u*,the estimation model’s fitting results revealed an increase in correlation coefficient(R2)from 0.51 to 0.62,and a decrease in root mean square error(RMSE)from 0.0652 to 0.0574.Additionally,when parameterizing W using the windsea Reynolds number,with u_(*turb) replacing u*and wind wave height substituting mixed wave height,the R^(2) increased from 0.38 to 0.53,and the RMSE decreased from 0.0737 to 0.0668.The results demonstrate that calculating u*using the turbulent stress-based method,along with wind wave height and peak wave speed of mixed waves,yields stronger correlation with W.This correlation improvement stems from the inhibition of wave breaking by swell and wave-induced stress.The integration of turbulent stress and wind wave field measurements enhances the understanding of relationships between W and various parameters.However,swell effects on wind profiles do not substantially affect W estimation using wind speed-related parameters.
基金National Natural Science Foundation of China(Grant No.52472417)to provide fund for conducting experiments.
文摘Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.
基金supported in part by the National Natural Science Foundation of China under Grants 62071189,62201220 and 62171189by the Key Research and Development Program of Hubei Province under Grant 2021BAA026 and 2020BAB120。
文摘As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
基金supported by the National Key Research and Development Program of China under Grant 2018YFA0701601by the Program of Jiangsu Province under Grant NTACT-2024-Z-001.
文摘In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.
文摘Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in the delivery of vaccination services.With the advent of COVID-19 pandemic,many countries have included community pharmacists and pharmacies in vaccination services to improve coverage.This study described the delivery of vaccination services in community pharmacies using the COVID-19 experience and how their involvement impacted vaccination coverage in Nigeria.It also exposed how this experience can be used to support policy revisions to formally recognize pharmacists in immunization delivery.Methods:A descriptive cross-sectional study was conducted among 474 community pharmacists in two southwestern States in Nigeria,using a semi-structured questionnaire.It determines the number of community pharmacists who have been trained in the delivery of vaccination services,the types of vaccination services provided,and vaccines administered in their pharmacies.Data were analyzed with descriptive and inferential statistics and p-value at≤0.05.Results:Response rate was 86.7%.Less than half of the respondents(40.1%)had undergone vaccination training.Of the 129(31.4%)respondents that provide vaccination services,72(55.8%)administer vaccines in their pharmacies.Out of these 72 respondents;45(62.5%)were administering vaccines before their involvement in COVID-19 vaccine administration;57(79.2%)of the health personnel who administer vaccines were pharmacists;60(83.3%)of them administer vaccines on request;22(30.6%)administered COVID-19 vaccines only;and only 7(9.7%)of the respondents had administered over 500 doses of COVID-19 vaccines.Training in vaccination was associated with the vaccination services provided(p<0.05).Respondents suggested government support through legal framework and policy review,training and empowering pharmacists in vaccine administration,and recognition of community pharmacists as PHC providers.
基金supported by the National Natural Science Foundation of China(Nos.61773142,62303136)China Postdoctoral Science Foundation(No.2023M740912)Postdoctoral Fellowship Program of CPSF,China(No.GZC20233447).
文摘In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.
